Search Results for: resolution

https://github.com/thunil/TecoGAN

 

https://github.com/zhyever/PatchFusion/tree/main

 

www.digitalarchns.com/refinemesh/

bernardsmith.eu/computatrum/scan_and_restore_archive_and_print/scanning/

https://www.urtech.ca/2019/04/solved-complete-list-of-screen-resolution-names-sizes-and-aspect-ratios/

 

Resolution – Aspect Ratio	4:03	16:09	16:10	3:02	5:03	5:04
CGA			320 x 200
QVGA	320 x 240
VGA (SD, Standard Definition)	640 x 480
NTSC				720 x 480
WVGA		854 x 450
WVGA					800 x 480
PAL	768 x 576
SVGA	800 x 600
XGA	1024 x 768
not named				1152 x 768
HD 720 (720P, High Definition)		1280 x 720
WXGA			1280 x 800
WXGA					1280 x 768
SXGA						1280 x 1024
not named (768P, HD, High Definition)		1366 x 768
not named				1440 x 960
SXGA+	1400 x 1050
WSXGA			1680 x 1050
UXGA (2MP)	1600 x 1200
HD1080 (1080P, Full HD)		1920 x 1080
WUXGA			1920 x 1200
2K		2048 x (any)
QWXGA		2048 x 1152
QXGA (3MP)	2048 x 1536
WQXGA			2560 x 1600
QHD (Quad HD)				2560 x 1440
QSXGA (5MP)						2560 x 2048
4K UHD (4K, Ultra HD, Ultra-High Definition)		3840 x 2160
QUXGA+			3840 x 2400
IMAX 3D	4096 x 3072
8K UHD (8K, 8K Ultra HD, UHDTV)		7680 x 4320
10K  (10240×4320, 10K HD)		10240 x (any)
16K (Quad UHD, 16K UHD, 8640P)		15360 x 8640

 

(more…)

 

https://www.popularmechanics.com/technology/security/a33013507/disney-deepfake-face-swap/

https://www.discovery.com/science/mexapixels-in-human-eye

About 576 megapixels for the entire field of view.

 

Consider a view in front of you that is 90 degrees by 90 degrees, like looking through an open window at a scene. The number of pixels would be:
90 degrees * 60 arc-minutes/degree * 1/0.3 * 90 * 60 * 1/0.3 = 324,000,000 pixels (324 megapixels).

 

At any one moment, you actually do not perceive that many pixels, but your eye moves around the scene to see all the detail you want. But the human eye really sees a larger field of view, close to 180 degrees. Let’s be conservative and use 120 degrees for the field of view. Then we would see:

120 * 120 * 60 * 60 / (0.3 * 0.3) = 576 megapixels.

Or.

7 megapixels for the 2 degree focus arc… + 1 megapixel for the rest.

https://clarkvision.com/articles/eye-resolution.html

 

 

How many megapixels do you really need?

https://www.tomsguide.com/us/how-many-megapixels-you-need,review-1974.html

 

 

www.domeble.com/

When collecting hdri make sure the data supports basic metadata, such as:

• Iso
• Aperture
• Exposure time or shutter time
• Color temperature
• Color space Exposure value (what the sensor receives of the sun intensity in lux)
• 7+ brackets (with 5 or 6 being the perceived balanced exposure)

 

In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.

 

The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography

 

In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.

 

The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.

 

HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.

 

igl.ethz.ch/projects/prosr/

http://iosres.com/

http://www.pixelsham.com/2019/09/18/aspect-ratios-in-film-how-to-choose-the-most-cinematic-aspect-ratio/

 

http://www.shutterangle.com/2012/cinematic-look-aspect-ratio-sensor-size-depth-of-field/

 

http://www.shutterangle.com/2012/film-video-aspect-ratio-artistic-choice/

 

https://www.cined.com/iphone-15-pro-anamorphic-experiment-what-makes-a-cinema-camera-by-michael-cioni-strada/

 

For Michael Cioni, a cinema camera has to fulfill five requisites:

 

• Cinematic resolution
• Intraframe encoding
• High dynamic range
• Wide color gamut (10-bit or more)
• Removable lenses

For now, the iPhone 15 Pro meets four out of these five requirements, all except the last one.

 

 

 

https://education.civitai.com/generative-ai-glossary/

 

Term	Tags	Description
.ckpt	Model	“Checkpoint”, a file format created by PyTorch Lightning, a PyTorch research framework. It contains a PyTorch Lightning machine learning model used (by Stable Diffusion) to generate images.
.pt	Software	A machine learning model file created using PyTorch, containing algorithms used to automatically perform a task.
.Safetensors	Model	A file format for Checkpoint models, less susceptible to embedded malicious code. See “Pickle”
ADetailer	Software, Extension	A popular Automatic1111 Extension, mostly used to enhance fine face and eye detail, but can be used to re-draw hands and full characters.
AGI	Concept	Artificial General Intelligence (AGI), the point at which AI matches or exceeds the intelligence of humans.
Algorithm	Concept, Software	A series of instructions that allow a computer to learn and analyze data, learning from it, and use that learning to interpret and accomplish future tasks on its own.
AnimateDiff	Software, Extension	Technique which involves injecting motion into txt2img (or img2img) generations. https://animatediff.github.io/
API	Software	Application Programmer Interface – a set of functions and tools which allow interaction with, or between, pieces of software.
Auto-GPT	Software, LLM
Automatic1111	Developer, SD User Interface	Creator of the popular Automatic1111 WebUI graphical user interface for SD.
Bard	Software, LLM	Google’s Chatbot, based on their LaMDA model.
Batch		A subset of the training data used in one iteration of model training. In inference, a group of images.
Bias	Concept, LLM	In Large Language Models, errors resulting from training data; stereotypes, attributing certain characteristics to races or groups of people, etc. Bias can cause models to generate offensive and harmful content.
Bing	Software, LLM	Microsoft’s ChatGTP powered Chatbot.
CFG	Setting	Classifier Free Guidance, sometimes “Guidance Scale”. Controls how closely the image generation process follows the text prompt.
Checkpoint	Model	The product of training on millions of captioned images scraped from multiple sources on the Web. This file drives Stable Diffusion’s txt2img, img2img, txt2video
Civitai (Civitai.com)	Community Resource	Popular hosting site for all types of Generative AI resources.
Civitai Generator	Software, Tool	Free Stable Diffusion Image Generation Interface, available on Civitai.com.
Civitai Trainer	Software, Tool	LoRA Training interface, available on Civitai.com, for SDXL and 1.5 based LoRA.
CLIP	Software	An open source model created by OpenAI. Trained on millions of images and captions, it determines how well a particular caption describes an image.
Cmdr2	Developer, SD User Interface	Creator of the popular EasyDiffusion, simple one-click install graphical user interface for SD.
CodeFormer	Face/Image Restoration, Model	A facial image restoration model, for fixing blurry, grainy, or disfigured faces.
Colab	Tool	Colaboratory, a product from Google Research, allowing execution of Python code through the browser. Particularly geared towards machine learning applications. https://colab.research.google.com/
ComfyUI	SD User Interface, Software	A popular powerful modular UI for Stable Diffusion with a “workflow” type workspace. Somewhat more complex than Auto1111 WebUI https://github.com/comfyanonymous/ComfyUI
CompVis	Organization	Computer Vision & Learning research group at Ludwig Maximilian University of Munich. They host Stable Diffusion models on Hugging Face.
Conda	Application, Software	An open source package manager for many programming languages, including Python.
ControlNet	UI Extension	An Extension to Auto1111 WebUI allowing images to be manipulated in a number of ways. https://github.com/Mikubill/sd-webui-controlnet
Convergence	Concept	The point in image generation where the image no longer changes as the steps increase.
CUDA	Hardware, Software	Compute Unified Device Architecture, Nvdia’s parallel processing architecture.
DALL-E / DALL-E 2	Organization	Deep learning image models created by OpenAI, available as a commercial image generation service.
Danbooru	Community Resource	English-based image board website specializing in erotic manga fan art, NSFW.
Danbooru Tag	Community Resource	System of keywords applied to Danbooru images describing the content within. When using Checkpoint models trained on Danbooru images, keywords can be referenced in Prompts.
DDIM (Sampler)	Sampler	Denoising Diffusion Implicit Models. See Samplers.
Deep Learning	Concept	A type of Machine Learning, where neural networks attempt to mimic the behavior of the human brain to perform tasks.
Deforum	UI Extension, Community Resource	A community of AI image synthesis developers, enthusiasts, and artists, producing Generative AI tools. Most commonly known for a Stable Diffusion WebUI video extension of the same name.
Denoising/Diffusion	Concept	The process by which random noise (see Seed) is iteratively reduced into the final image.
depth2img	Concept	Infers the depth of an input image (using an existing model), and then generates new images using both the text and depth information.
Diffusion Model (DM)	Model	A generative model, used to generate data similar to the data on which they are trained.
DPM adaptive (Sampler)	Sampler	Diffusion Probabilistic Model (Adaptive). See Samplers. Ignores Step Count.
DPM Fast (Sampler)	Sampler	Diffusion Probabilistic Model (Fast). See Samplers.
DPM++ 2M (Sampler)	Sampler	Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps.
DPM++ 2M Karras (Sampler)	Sampler	Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps.
DPM++ 2S a Karras (Sampler)	Sampler	Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps.
DPM++ 2Sa (Sampler)	Sampler	Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps.
DPM++ SDE (Sampler)	Sampler
DPM++ SDE Karras (Sampler)	Sampler
DPM2 (Sampler)	Sampler
DPM2 a (Sampler)	Sampler
DPM2 a Karras (Sampler)	Sampler
DPM2 Karras (Sampler)	Sampler
DreamArtist	UI Extension, Software	An extension to WebUI allowing users to create trained embeddings to direct an image towards a particular style, or figure. A PyTorch implementation of the research paper DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning, Ziyi Dong, Pengxu Wei, Liang Lin.
DreamBooth	Software, Community Resource	Developed by Google Researchers, DreamBooth is a deep learning image generation model designed to fine-tune existing models (checkpoints). Can be used to create custom models based on a set of images.
DreamStudio	Organization, SD User Interface	A commercial web-based image generation service created by Stability AI using Stable Diffusion models.
Dropout (training)	Concept	A technique to prevent overfitting by randomly ignoring some images/tokens, etc. during training.
DyLoRA C3Lier
DyLoRA LierLa
DyLoRA Lycoris
EMA	Model	Exponential Moving Average. A full EMA Checkpoint model contains extra training data which is not required for inference (generating images). Full EMA models can be used to further train a Checkpoint.
Emad	Organization, Developer	Emad Mostaque, CEO and co-founder of Stability AI, one of the companies behind Stable Diffusion.
Embedding	Model, UI Extension	Additional file inputs to help guide the diffusion model to produce images that match the prompt. Can be a graphical style, representation of a person, or object. See Textual Inversion and Aesthetic Gradient.
Emergent Behavior	Concept, LLM	Unintended abilities exhibited by an AI model.
Entropy	Concept	A measure of randomness, or disorder.
Epoch	Concept	The number of times a model training process looked through a full data set of images. E.g. The 5th Epoc of a Checkpoint model looked five times through the same data set of images.
ESRGAN	Upscaler, Model	Enhanced Super-Resolution Generative Adversarial Networks. A technique to reconstruct a higher-resolution image from a lower-resolution image. E.g. upscaling of a 720p image into 1080p. Implemented as a tool within many Stable Diffusion interfaces.
Euler (Sampler)	Sampler	Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers.
Euler a (Sampler)	Sampler	Ancestral version of the Euler sampler. Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers.
Finetune	Concept
float16	Setting, Model, Concept	Half-Precision floating point number.
float32	Setting, Model, Concept	Full-Precision floating point number.
Generative Adversarial Networks (GANs)	Model	A pair of AI models: one generates new data, and the other evaluates its quality.
Generative AI	Concept
GFPGAN	Face/Image Restoration, Model	Generative Facial Prior, a facial restoration model for fixing blurry, grainy, or disfigured faces.
Git (GitHub)	Application, Software	Hosting service for software development, version control, bug tracking, documentation.
GPT-3	Model, LLM	Generative Pre-trained Transformer 3, a language model, using machine learning to produce human-like text, based on an initial prompt.
GPT-4	Model, LLM	Generative Pre-trained Transformer 4, a language model, using machine learning to produce human-like text, based on an initial prompt. A huge leap in performance and reasoning capability over GPT 3/3.5.
GPU	Hardware	A Graphics Processing Unit, a type of processor designed to perform quick mathematical calculations, allowing it to render images and video for display.
Gradio	Software	A web-browser based interface framework, specifically for Machine Learning applications. Auto1111 WebUI runs in a Gradio interface.
Hallucinations (LLM)	LLM, Concept	Sometimes LLM models like ChatGPT produce information that sounds plausible but is nonsensical or entirely false. This is called a Hallucination.
Hash (Checkpoint model)	Model, Concept	An algorithm for verifying the integrity of a file, by generating an alphanumeric string unique to the file in question. Checkpoint models are hashed, and the resulting string can be used to identify that model.
Heun (Sampler)	Sampler	Named after Karl Heun, a numerical procedure for solving ordinary differential equations. See Samplers.
Hugging Face	Organization	A community/data science platform providing tools to build, train, and deploy machine learning models.
Hypernetwork (Hypernet)	Model	A method to guide a Checkpoint model towards a specific theme, object, or character based on its’ own content (no external data required).
img2img	Concept	Process to generate new images based on an input image, and txt2img prompt.
Inpainting	Concept	The practice of removing or replacing objects in an image based on a painted mask.
Kohya	Software	Can refer to Kohya-ss scripts for LoRA/finetuning (https://github.com/kohya-ss/sd-scripts) or the Windows GUI implementation of those scripts (https://github.com/bmaltais/kohya_ss)
LAION	Organization	A non-profit organization, providing data sets, tools, and models, for machine learning research.
LAION-5B	Model	A large-scale dataset for research purposes consisting of 5.85 billion CLIP-filtered image-text pairs.
Lanczos	Upscaler, Model	An interpolation method used to compute new values for sampled data. In this case, used to upscale images. Named after creator, Cornelius Lanczos.
Large Language Model (LLM)	LLM, Model	A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research.
Latent Diffusion	Model	A type of diffusion model that contains compressed image representations instead of the actual images. This type of model allows the storage of a large amount of data that can be used by encoders to reconstruct images from textual or image inputs.
Latent Mirroring	Concept, UI Extension	Applies mirroring to the latent images mid-generation to produce anything from subtly balanced compositions to perfect reflections.
Latent Space	Concept	The information-dense space where the diffusion model’s image representation, attention, and transformation are merged and form the initial noise for the diffusion process.
LDSR	Upscaler	Latent Diffusion Super Resolution upscaling. A method to increase the dimensions/quality of images.
Lexica	Community Resource	Lexica.art, a search engine for stable diffusion art and prompts.
LlamaIndex (GPT Index)	Software, LLM	https://github.com/jerryjliu/llama_index – Allows the connection of text data to an LLM via a generated “index”.
LLM	LLM, Model	A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research.
LMS (Sampler)	Sampler
LMS Karras (Sampler)	Sampler
LoCON
LoHa
LoKR
LoRA	Model, Concept	Low-Rank Adaptation, a method of training for SD, much like Textual Inversion. Can capture styles and subjects, producing better results in a shorter time, with smaller output files, than traditional finetuning.
LoRA C3Lier
LoRA LierLa
Loss (function)	Concept	A measure of how well an AI model’s outputs match the desired outputs.
Merge (Checkpoint)	Model	A process by which Checkpoint models are combined (merged) to form new models. Depending on the merge method (see Weighted Sum, Sigmoid) and multiplier, the merged model will retain varying characteristics of its’ constituent models.
Metadata	Concept, Software	Metadata is data that describes data. In the context of Stable Diffusion, metadata is often used to describe the Prompt, Sampler settings, CFG, steps, etc. which are used to define an image, and stored in a .png header.
MidJourney	Organization, SD User Interface	A commercial web-based image generation service, similar to DALL-E, or the free, open source, Stable Diffusion.
Model	Model	Alternative term for Checkpoint
Motion Module	Software	Used by AnimateDiff to inject motion into txt2img (or img2img) generations.
Multimodal AI	Concept	AI that can process multiple types of inputs, including text, images, video or speech.
Negative Prompt	Setting, Concept	Keywords which tell a Stable Diffusion prompt what we don’t want to see, in the generated image.
Neural Network	Concept, Software	Mathematical systems that act like a human brain, with layers of artificial “neurons” helping find connections between data.
Notebook	Community Resource, Software	See Colab. A Jupyter notebook service providing access, free of charge, to computing resources including GPUs.
NovelAI (NAI)	Organization	A paid, subscription based AI-assisted story (text) writing service. Also has a txt2img model, which was leaked and is now incorporated into many Stable Diffusion models.
Olivio (Sarikas)	Community Resource	Olivio produces wonderful SD content on YouTube (https://www.youtube.com/@OlivioSarikas) – one of the best SD news YouTubers out there!
OpenAI	Organization	AI research laboratory consisting of the for-profit corporation OpenAI LP and the non-profit OpenAI Inc.
OpenPose	Model, Software	A method for extracting a “skeleton” from an image of a person, allowing poses to be transferred from one image to another. Used by ControlNet.
Outpainting	Concept	The practice of extending the outer border of an image, into blank canvas space, while maintaining the style and content of the image.
Overfitting	Concept	When an AI model learns the training data too well and performs poorly on unseen data.
Parameters (LLMs)	Concept, Software, LLM	Numerical points across a Large Language Model’s training data. Parameters dictate how proficient the model is at its tasks. E.g. a 6B (Billion) Parameter model will likely perform less well than a 13B Parameter model.
Pickle	Concept, Software	Community slang term for potentially malicious code hidden within models and embeddings. To be “pickled” is to have unwanted code execute on your machine (be hacked).
PLMS (Sampler)	Sampler	Pre-Trained Language Models. See Samplers.
Prompt	Concept	Text input to Stable Diffusion describing the particulars of the image you would like output.
Pruned/Pruning	Model	A method of optimizing a Checkpoint model to increase the speed of inference (prompt generation), file size, and VRAM cost.
Python	Application, Software	A popular, high-level, general purpose coding language.
PyTorch	Application, Software	An open source machine learning library, created by META.
Real-ESRGAN	Upscaler	An image restoration method.
Refiner	Model	Part of SDXL’s two-stage pipeline – the Refiner further enhances detail from the base model.
SadTalker	UI Extension	https://github.com/OpenTalker/SadTalker A framework for facial animation/lip synching based upon an audio input.
Samplers	Sampler	Mathematical functions providing different ways of solving differential equations. Each will produce a slightly (or significantly) different image result from the random latent noise generation.
Sampling Steps	Sampler, Concept	The number of how many steps to spend generating (diffusing) your image.
SD 1.4	Model	A latent txt2img model, the default model for SD at release. Fine-tuned on 225k steps at resolution 512×512 on laion-aesthetics v2 data set.
SD 1.5	Model	A latent txt2img model, updated version of 1.4, fine-tuned on 595k steps at resolution 512×512 on laion-aesthetics v2 data set.
SD UI	Application, Software	Colloquial term for Cmdr2’s popular graphical interface for Stable Diffusion prompting.
SD.Next	Software	See Vlad, Vladmandic Fork of Auto1111 WebUI.
SDXL 0.9	Model	Stability AI’s latest (March 2023) Stable Diffusion Model. Will become SDXL 1.0 and be released ~July 2023.
Seed	Concept	A pseudo-random number used to initialize the generation of random noise, from which the final image is built. Seeds can be saved and used along with other settings to recreate a particular image.
Shoggoth Tongue	Concept, LLM	A humorous allusion to the language of the fictional monsters in the Cthulhu Mythos, “Shoggoth Tongue” is the name given to advanced ChatGPT commands which are particularly arcane and difficult to understand, but allow ChatGPT to perform advanced actions outside of the intended operation of the system.
Sigmoid (Interpolation Method)	Model, Concept	A method for merging Checkpoint Models based on a Sigmoid function – a mathematical function producing an “S” shaped curve.
Stability AI	Organization	AI technology company co-founded by Emad Mostaque. One of the companies behind Stable Diffusion.
Stable Diffusion (SD)	Application, Software	A deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images based on provided text descriptions.
SwinIR	Face/Image Restoration, Model	An image restoration transform, aiming to restore high quality images from low quality images.
Tensor	Software	A container, in which multi-dimensional data can be stored.
Tensor Core	Hardware	Processing unit technology developed by Nvidia, designed to carry out matrix multiplication, an arithmetic operation.
Textual Inversion	Model, Concept, UI Extension	A technique for capturing concepts from a small number of sample images in a way that can influence txt2img results towards a particular face, or object.
Token	Concept	A token is roughly a word, a punctuation, or a Unicode character in a prompt.
Tokenizer	Concept, Model	The process/model through which text prompts are turned into tokens, for processing.
Torch 2.0	Software	The latest (March 2023) PyTorch release.
Training	Concept	The process of teaching an AI model by feeding it data and adjusting its parameters.
Training Data	Model	A set of many images used to “train” a Stable Diffusion model, or embedding.
Training Data	Concept, LLM, Model	The data sets uses to help AI models learn; can be text, images, code, or other data, depending on the type of model to be trained.
Turing Test	Concept	Named after mathematician Alan Turing, a test of a machine’s ability to behave like a human. The machine passes if a human can’t distinguish the machine’s response from another human.
txt2img	Concept, Model	Model/method of image generation via entry of text input.
txt2video	Concept, Model	Model/method of video generation via entry of text input.
Underfitting		When an AI model cannot capture the underlying pattern of the data due to incomplete training.
UniPC (Sampler)	Sampler	A recently released (3/2023) sampler based upon https://huggingface.co/docs/diffusers/api/schedulers/unipc
Upscale	Upscaler, Concept	The process of converting low resolution media (images or video) into higher resolution media.
VAE	Model	Variational Autoencoder. A .vae.pt file which accompanies a Checkpoint model and provides additional detail improvements. Not all Checkpoints have an associated vae file, and some vae files are generic and can be used to improve any Checkpoint model.
Vector (Prompt Word)	Concept	An attempt to mathematically represent the meaning of a word, for processing in Stable Diffusion.
Venv	Software	A Python “Virtual Environment” which allows multiple instances of python packages to run, independently, on the same PC.
Vicuna	LLM, Software, Model	https://vicuna.lmsys.org/ An Open-Source Chatbot model founded by students and faculty from UC Berkeley in collaboration with UCSD and CMU.
Vladmandic	Software, SD User Interface	A popular “Fork” of Auto1111 WebUI, with its own feature-set. https://github.com/vladmandic/automatic
VRAM	Hardware	Video random access memory. Dedicated Graphics Card (GPU) memory used to store pixels, and other graphical processing data, for display.
Waifu Diffusion	Model	A popular text-to-image model, trained on high quality anime images, which produces anime style image outputs. Originally produced for SD 1.4, now has an SDXL version.
WebUI	Application, Software, SD User Interface	Colloquial term for Automatic1111’s WebUI – a popular graphical interface for Stable Diffusion prompting.
Weighted Sum (Interpolation Method)	Concept	A method of Checkpoint merging using the formula Result = ( A * (1 – M) ) + ( B * M ) .
Weights	Model	Alternative term for Checkpoint
Wildcards	Concept	Text files containing terms (clothing types, cities, weather conditions, etc.) which can be automatically input into image prompts, for a huge variety of dynamic images.
xformers	UI Extension, Software	Optional library to speed up image generation. Superseded somewhat by new options implemented by Torch 2.0
yaml	Software, UI Extension, Model	A human-readable data-serialization programming language commonly used for configuration files. Yaml files accompany Checkpoint models, and provide Stable Diffusion with additional information about the Checkpoint.

https://blog.frame.io/2024/02/01/how-to-capture-and-view-vision-pro-spatial-video/

 

Apple’s Immersive Videos format is a special container for 3D or “spatial” video. You can capture spatial video to this format either by using the Vision Pro as a head-mounted camera, or with an iPhone 15 Pro or 15 Pro Max. The headset offers better capture because its cameras are more optimized for 3D, resulting in higher resolution and improved depth effects.

 

While the iPhone wasn’t designed specifically as a 3D camera, it can use its primary and ultrawide cameras in landscape orientation simultaneously, allowing it to capture spatial video—as long as you hold it horizontally. Computational photography is used to compensate for the lens differences, and the output is two separate 1080p, 30fps videos that capture a 180-degree field of view.

 

These spatial videos are stored using the MV-HEVC (Multi-View High-Efficiency Video Coding) format, which uses H.265 compression to crunch this down to approximately 130MB per minute, including spatial audio. Unlike conventional stereoscopic formats—which combine the two views into a flattened video file that’s either side-by-side or top/bottom—these spatial videos are stored as discrete tracks within the file container.

 

Spatialify is an iOS app designed to view and convert various 3D formats. It also works well on Mac OS, as long as your Mac has an Apple Silicon CPU. And it supports MV-HEVC, so you’ll be all set. It’s just $4.99, a genuine bargain considering what it does. Find Spatialify here.

 

 

https://www.boredpanda.com/high-resolution-sun-pictures-alan-friedman/

 

https://avertedimagination.squarespace.com/

 

He uses a small (3 ½” aperture) telescope with a Hydrogen Alpha filter and an industrial webcam to capture the surface of the Sun, which looks surprisingly calm and fluffy in the incredible photos.

 

 

https://thecamerastore.com/products/canon-rf-5-2mm-f2-8l-dual-fisheye

 

As part of the EOS VR System – this lens paired with the EOS R5 updated with firmware 1.5.0 or higher and one of Canon’s VR software solutions – you can create immersive 3D that can be experienced when viewed on compatible head mount displays including the Oculus Quest 2 and more. Viewers will be able to take in the scene with a vivid, wide field of view by simply moving their head. This is the world’s first digital interchangeable lens that can capture stereoscopic 3D 180° VR imagery to a single image sensor.

 

The pairing of this lens and the EOS R5 camera brings high resolution video recording at up to 8K DCI 30p and 4K DCI 60p.

 

https://www.the-digital-picture.com/Reviews/Canon-RF-5.2mm-F2.8-L-Dual-Fisheye-Lens.aspx

 

 

 

 

https://theasc.com/articles/sphere-and-the-big-sky-camera

 

Sphere is a 516′-wide, 366′-tall geodesic dome that houses the world’s highest-resolution screen: a 160,000-square-foot LED wraparound that fills the peripheral vision for 17,600 spectators (20,000 if standing-room areas are included). The curved screen is a 9mm-pixel-pitch, sonically transparent surface of LED panels with 500-nit brightness that produce a high-dynamic-range experience. The audience sits 160′ to 400′ from the screen in theatrical seating, and the screen provides a 155-degree diagonal field of view and a more-than-140-degree vertical field of view.

 

The image on the screen is 16K (16,384x16,384) driven by 25 synchronized 4K video servers.

 

https://nofilmschool.com/darren-aronofsky-sphere-camera

 

 

Cross section:

 

 

 

https://www.roadtovr.com/meta-quest-3-oculus-preview-connect-2023/

 

• Better lenses
• Better resolution
• Better processor
• Better audio
• Better passthrough
• Better controllers
• Better form-factor

 

 

 

https://stability.ai/blog/stable-diffusion-v2-release

 

 

• New Text-to-Image Diffusion Models
• Super-resolution Upscaler Diffusion Models
• Depth-to-Image Diffusion Model
• Updated Inpainting Diffusion Model

 

https://www.reddit.com/r/StableDiffusion/comments/z64aup/realistic_prompts_using_tomlikesrobots_workflow/

 

 

https://waxy.org/2022/09/ai-data-laundering-how-academic-and-nonprofit-researchers-shield-tech-companies-from-accountability/

 

“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”

 

“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”

 

“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”

 

“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”

First introduced in 2012, nowadays OpenVDB is commonly applied in simulation tools such as Houdini, EmberGen, Blender, and used in feature film production for creating realistic volumetric images. This format, however, lacks the GPUs support and can not be applied in games due to the considerable file size (on average at least a few Gigabytes) and computational effort required to render 3D volumes.

Volumetric data has numerous important applications in computer graphics and VFX production. It’s used for volume rendering, fluid simulation, fracture simulation, modeling with implicit surfaces, etc. However, this data is not so easy to work with. In most cases volumetric data is represented on spatially uniform, regular 3D grids. Although dense regular grids are convenient for several reasons, they have one major drawback – their memory footprint grows cubically with respect to grid resolution.

OpenVDB format, developed by DreamWorksAnimation, partially solves this issue by storing voxel data in a tree-like data structure that allows the creation of sparse volumes. The beauty behind this system is that it completely ignores empty cells, which drastically decreases memory and disk usage, simultaneously making the rendering of volumes much faster.

 

 

www.aswf.io/blog/project-update-openvdb-version-9-0-0-available-now-introduces-gpu-support/

 

github.com/AcademySoftwareFoundation/openvdb/releases/tag/v9.0.0

www.alexanderrichtertd.com/post/plex-open-source-pipeline

Environments
– OS: Windows | Linux | Mac
– Software: Maya 2020+ | Houdini 15+ | 3ds Max 2020+ | Nuke 12+ | …
– Renderer: Arnold | RenderMan | Mantra | V-Ray | …

Project Features
– Visual Effects, Animation & Game production management system
– file & folder management (settings | create | save | load | publish)
– flexible, portable, multi functional project environment
– additional libraries (api | img | user | shot)
– workflow tracking & reporting
– user-pipeline integration
– SSTP (simple | smart | transparent | performant)

Pipeline Features

Layered Pipeline
– create a company pipeline
– add a project pipeline
– test and develop in a personal environment

Scripts
– desktop app
– save (+ publish) | load | create | render
– get, set and handle data | img | scripts
– template UI (user, report, help, accept, comment, color code)
– setup menu, shelf, toolbar, …

Workflows and Charts
– naming conventions
– software pipeline
– folder structure (project & pipeline)

Data and Helper
– project (resolution, fps …)
– user (name, task …)
– context (shot, task, comment …)
– environment variables (PROJECT_PATH …)
– additional libraries

Feedback & Debug (+ advanced logging)
– inform user about processes
– debug like a king *bow*

www.cgdirector.com/best-monitor-graphic-design-video-editing-3d/

There are three main Panel Types found in today’s modern Monitors.

The TN Panel (Twisted nematic)
The VA Panel (Vertical Alignment)
The IPS Panel (In-plane Switching)

 

The IPS Panel is the best panel type for visually demanding work.

 

imagescience.com.au/products/monitors/monitors-for-video-editing-and-vfx

 

The Eizo CG319X is the current benchmark monitor for video work from Eizo, the most respected name in the high end colour accurate monitor business.

 

Used by some of the world’s best VFX studios – like WETA Digital and Studio Ghibli – this full true 4K monster offers superb accuracy, DCI true blacks, and fully automatic calibration with a in-built high quality calibration sensor, very generous working area, and true full 4K resolution. It can show nearly all of the DCI-P3 colour space with extreme precision.

 

If you’ve got the budget, this is without doubt the monitor to own for high end video and FX work.

 

imagescience.com.au/products/monitors/eizo-coloredge-cg319x-4k

Samsung The Wall

 

 

The Wall TV can be configured to sizes ranging from 146 inches to 292 inches diagonally and uses MicroLED technology instead of OLED or traditional LED.

MicroLED delivers many of the benefits you’ll find in OLED, including perfect blacks and eye-popping colors, but the set also boasts 1,600 nits of brightness. That’s brighter than today’s OLED sets.

Currently, Samsung is offering two models of The Wall, or rather the individual panels that make up The Wall, the IW008J and the IW008R. While Samsung doesn’t list prices for these panels online, other resellers are listing the modules for $16 to $23 thousand dollars each.

These individual modules measure 31.75 x 17.86 inches, but have an individual resolution of 960 x 540 pixels. In order to enjoy the same 3840 x 2160 resolution you’ll get on a standard 4K TV, you’ll need to buy 16 of these panels, to set up in a 4 x 4 configuration that measures 146 inches diagonally.

If you’re in the market for a microLED TV, and are comfortable spending upwards of $300,000 to get the same 4K resolution that the best cheap 4K TVs provide, you’ll need to contact Samsung directly to order products and arrange custom installation.

 

Because The Wall is made up of borderless tiles, the modular design allows additional tiles to be added, making this even-bigger version of The Wall possible.

 

https://www.tomsguide.com/us/samsung-the-wall-tv-release-date,news-27356.html

RASTERIZATION
Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.

For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.

In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.

With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.

Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.

Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.

 

The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.

There are also multiple problems:

• If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.

• The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.

• You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)

• Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)

 

 

 

RAY CASTING
It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.

The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.

You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.

 

RAYTRACING

Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.

The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.

Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.

Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.

Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.

 

 

PATH TRACING
Path tracing can be used to solve more complex lighting situations.
Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.

When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.

It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.

Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.

 

Sources:
https://medium.com/@junyingw/future-of-gaming-rasterization-vs-ray-tracing-vs-path-tracing-32b334510f1f

 

https://www.reddit.com/r/explainlikeimfive/comments/8tim5q/eli5_whats_the_difference_among_rasterization_ray/

 

blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/

 

https://en.wikipedia.org/wiki/Rasterisation

 

https://www.dusterwald.com/2016/07/path-tracing-vs-ray-tracing/

 

https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing

 

 

 

https://www.quora.com/What-is-the-comparison-between-the-human-eye-and-a-digital-camera

 

https://medium.com/hipster-color-science/a-beginners-guide-to-colorimetry-401f1830b65a

 

There are three types of cone photoreceptors in the eye, called Long, Medium and Short. These contribute to color discrimination. They are all sensitive to different, yet overlapping, wavelengths of light. They are commonly associated with the color they are most sensitive too, L = red, M = green, S = blue.

 

Different spectral distributions can stimulate the cones in the exact same way
A leaf and a green car that look the same to you, but physically have different reflectance properties. It turns out every color (or, unique cone output) can be created from many different spectral distributions. Color science starts to make a lot more sense when you understand this.

 

When you view the charts overlaid, you can see that the spinach mostly reflects light outside of the eye’s visual range, and inside our range it mostly reflects light centered around our M cone.

 

This phenomenon is called metamerism and it has huge ramifications for color reproduction. It means we don’t need the original light to reproduce an observed color.

 

http://www.absoluteastronomy.com/topics/Adaptation_%28eye%29

 

The human eye can function from very dark to very bright levels of light; its sensing capabilities reach across nine orders of magnitude. This means that the brightest and the darkest light signal that the eye can sense are a factor of roughly 1,000,000,000 apart. However, in any given moment of time, the eye can only sense a contrast ratio of one thousand. What enables the wider reach is that the eye adapts its definition of what is black. The light level that is interpreted as “black” can be shifted across six orders of magnitude—a factor of one million.

 

https://clarkvision.com/articles/eye-resolution.html

 

The Human eye is able to function in bright sunlight and view faint starlight, a range of more than 100 million to one. The Blackwell (1946) data covered a brightness range of 10 million and did not include intensities brighter than about the full Moon. The full range of adaptability is on the order of a billion to 1. But this is like saying a camera can function over a similar range by adjusting the ISO gain, aperture and exposure time.

In any one view, the eye eye can see over a 10,000 range in contrast detection, but it depends on the scene brightness, with the range decreasing with lower contrast targets. The eye is a contrast detector, not an absolute detector like the sensor in a digital camera, thus the distinction.  The range of the human eye is greater than any film or consumer digital camera.

As for DSLR cameras’ contrast ratio ranges in 2048:1.

 

(Daniel Frank) Several key differences stand out for me (among many):

• The area devoted to seeing detail in the eye — the fovea — is extremely small compared to a digital camera sensor. It covers a roughly circular area of only about three degrees of arc. By contrast, a “normal” 50mm lens (so called because it supposedly mimic the perspective of the human eye) covers roughly 40 degrees of arc. Because of this extremely narrow field of view, the eye is constantly making small movements (“saccades”) to scan more of the field, and the brain is building up the illusion of a wider, detailed picture.
• The eye has two different main types of light detecting elements: rods and cones. Rods are more sensitive, and detect only variations in brightness, but not color. Cones sense color, but only work in brighter light. That’s why very dim scenes look desaturated, in shades of gray, to the human eye. If you take a picture in moonlight with a very high-ISO digital camera, you’ll be struck by how saturated the colors are in that picture — it looks like daylight. We think of this difference in color intensity as being inherent in dark scenes, but that’s not true — it’s actually the limitation of the cones in our eyes.
• There are specific cones in the eye with stronger responses to the different wavelengths corresponding to red, green, and blue light. By contrast, the CCD or CMOS sensor in a color digital camera can only sense luminance differences: it just counts photons in tens of millions of tiny photodetectors (“wells”) spread across its surface. In front of this detector is an array of microscopic red, blue, and green filters, one per well. The processing engine in the camera interpolates the luminance of adjacent red-, green-, or blue-filtered detectors based on a so-called “demosaicing” algorithm. This bears no resemblance to how the eye detects color. (The so-called “foveon” sensor sold by Sigma in some of its cameras avoid demosaicing by layering different color-sensing layers, but this still isn’t how the eye works.)
• The files output by color digital cameras contain three channels of luminance data: red, green, and blue. While the human eye has red, green, and blue-sensing cones, those cones are cross-wired in the retina to produce a luminance channel plus a red-green and a blue-yellow channel, and it’s data in that color space (known technically as “LAB”) that goes to the brain. That’s why we can’t perceive a reddish-green or a yellowish-blue, whereas such colors can be represented in the RGB color space used by digital cameras.
• The retina is much larger than the fovea, but the light-sensitive areas outside the fovea, and the nuclei to which they wire in the brain, are highly sensitive to motion, particularly in the periphery of our vision. The human visual system — including the eye — is highly adapted to detecting and analyzing potential threats coming at us from outside our central vision, and priming the brain and body to respond. These functions and systems have no analogue in any digital camera system.

SUMMARY

1. A lot of 360 technology is natively supported in Unity3D. Examples here: https://assetstore.unity.com/packages/essentials/tutorial-projects/vr-samples-51519
2. Use the Google Cardboard VR API to export for Android or iOS. https://developers.google.com/vr/?hl=en https://developers.google.com/vr/develop/unity/get-started-ios
3. Images and videos are for the most equirectangular 2:1 360 captures, mapped onto a skybox (stills) or an inverted sphere (videos). Panoramas are also supported.
4. Stereo is achieved in different formats, but mostly with a 2:1 over-under layout.
5. Videos can be streamed from a server.
6. You can export 360 mono/stereo stills/videos from Unity3D with VR Panorama.
7. 4K is probably the best average resolution size for mobiles.
8. Interaction can be driven through the Google API gaze scripts/plugins or through Google Cloud Speech Recognition (paid service, https://assetstore.unity.com/packages/add-ons/machinelearning/google-cloud-speech-recognition-vr-ar-desktop-desktop-72625 )

---

DETAILS

• Google VR game to iOS in 15 minutes

• Step by Step Google VR and responding to events with Unity3D 2017.x

https://boostlog.io/@mohammedalsayedomar/create-cardboard-apps-in-unity-5ac8f81e47018500491f38c8
https://www.sitepoint.com/building-a-google-cardboard-vr-app-in-unity/

• Gaze interaction examples
  https://assetstore.unity.com/packages/tools/gui/gaze-ui-for-canvas-70881
  https://s3.amazonaws.com/xrcommunity/tutorials/vrgazecontrol/VRGazeControl.unitypackage
  https://assetstore.unity.com/packages/tools/gui/cardboard-vr-touchless-menu-trigger-58897

• Basics details about equirectangular 2:1 360 images and videos.
• Skybox cubemap texturing, shading and camera component for stills.
• Video player component on a sphere’s with a flipped normals shader.
• Note that you can also use a pre-modeled sphere with inverted normals.
• Note that for audio you will need an audio component on the sphere model.

• Setup a Full 360 stereoscopic video playback using an over-under layout split onto two cameras.
• Note you cannot generate a stereoscopic image from two 360 captures, it has to be done through a dedicated consumer rig.
  http://bernieroehl.com/360stereoinunity/

VR Actions for Playmaker
https://assetstore.unity.com/packages/tools/vr-actions-for-playmaker-52109

100 Best Unity3d VR Assets
http://meta-guide.com/embodiment/100-best-unity3d-vr-assets

…find more tutorials/reference under this blog page
(more…)

Depth of field is the range within which focusing is resolved in a photo.
Aperture has a huge affect on to the depth of field.

 

 

Changing the f-stops (f/#) of a lens will change aperture and as such the DOF.

f-stops are a just certain number which is telling you the size of the aperture. That’s how f-stop is related to aperture (and DOF).

If you increase f-stops, it will increase DOF, the area in focus (and decrease the aperture). On the other hand, decreasing the f-stop it will decrease DOF (and increase the aperture).

The red cone in the figure is an angular representation of the resolution of the system. Versus the dotted lines, which indicate the aperture coverage. Where the lines of the two cones intersect defines the total range of the depth of field.

This image explains why the longer the depth of field, the greater the range of clarity.

https://www.cnet.com/news/what-is-oled-and-what-can-it-do-for-your-tv/

OLED stands for Organic Light Emitting Diode. Each pixel in an OLED display is made of a material that glows when you jab it with electricity. Kind of like the heating elements in a toaster, but with less heat and better resolution. This effect is called electroluminescence, which is one of those delightful words that is big, but actually makes sense: “electro” for electricity, “lumin” for light and “escence” for, well, basically “essence.”

OLED TV marketing often claims “infinite” contrast ratios, and while that might sound like typical hyperbole, it’s one of the extremely rare instances where such claims are actually true. Since OLED can produce a perfect black, emitting no light whatsoever, its contrast ratio (expressed as the brightest white divided by the darkest black) is technically infinite.

OLED is the only technology capable of absolute blacks and extremely bright whites on a per-pixel basis. LCD definitely can’t do that, and even the vaunted, beloved, dearly departed plasma couldn’t do absolute blacks.

-Full Resolution: 2400x550px

-Projection surface approx.10×2 meters by aligning 3 projectors

-Matrox TripleHead2Go

-Computer to play quicktime in loop mode

http://www.jeffdesom.com/hitch/

http://www.flickr.com/photos/gsfc/6760135001/sizes/o/in/photostream/

http://gizmodo.com/5879142/the-most-amazing-and-stunning-highest-resolution-image-of-earth-ever