17988216. JUST NOTICEABLE DIFFERENCES-BASED VIDEO ENCODING simplified abstract (Apple Inc.)
Contents
JUST NOTICEABLE DIFFERENCES-BASED VIDEO ENCODING
Organization Name
Inventor(s)
Hye-Yeon Cheong of Los Gatos CA (US)
Jiancong Luo of San Diego CA (US)
Linfeng Guo of Cupertino CA (US)
JUST NOTICEABLE DIFFERENCES-BASED VIDEO ENCODING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17988216 titled 'JUST NOTICEABLE DIFFERENCES-BASED VIDEO ENCODING
Simplified Explanation
Techniques for High Efficiency Video Coding with JND Models
- Predictive coding of input pixel blocks with reference to a prediction reference
- Transformation of prediction residuals into transform domain coefficients
- Quantization of transform coefficients using JND-quality quantization values
- Indexing quantization parameters from a table based on statistical analysis of input pixel block
Potential Applications:
This technology can be applied in various video coding applications such as video streaming services, video conferencing systems, surveillance systems, and video editing software.
Problems Solved:
1. Achieving high coding efficiency while retaining high image quality in video coding applications. 2. Estimating coding artifacts accurately using Just Noticeable Difference (JND) models.
Benefits:
1. Improved video coding efficiency leading to reduced bandwidth requirements. 2. Enhanced image quality in compressed videos. 3. Accurate estimation of coding artifacts for better video quality assessment.
Potential Commercial Applications:
"Optimizing Video Coding Efficiency with JND Models"
Possible Prior Art:
One possible prior art in this field is the use of perceptual models in video coding to improve compression efficiency and image quality. Researchers have explored the integration of human visual system characteristics into video coding algorithms to achieve better compression performance.
Unanswered Questions:
1. How do these techniques compare to existing video coding standards in terms of compression efficiency and image quality? 2. Are there any limitations or challenges in implementing JND models for quantization in video coding applications?
Original Abstract Submitted
Techniques are disclosed for achieving quantization in video coding applications that achieves high coding efficiency and retains high image quality. These techniques employ quantization processes using quantization parameters that have been developed according to Just Noticeable Difference (“JND”) models for estimating coding artifacts from video coding. According to these techniques, an input pixel block of video is predictively coded with reference to a prediction reference, and prediction residuals obtained therefrom are transformed to transform domain coefficients. A transform coefficient is quantized by a quantization parameter read from a table populated by JND-quality quantization values, which is indexed by a value representing a statistical analysis of the input pixel block.
