Adaptive Quantization for Video Coding Using Psychovisual Analysis
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Solution Overview
Problem
Current video encoding techniques struggle to achieve high subjective quality while maintaining compliance with existing standards like ITU-T H.264/ISO MPEG AVC and ITU-T H.265/ISO MPEG HEVC, as they often prioritize bitrate efficiency over visual quality, leading to suboptimal performance in encoding video content.
Innovation Solution
The implementation of adaptive quantization processes that utilize psychovisual sensitivity analysis and content adaptive lambda factor adaptation to optimize bitstreams, focusing on human regions of interest and long-term persistence, allowing for improved subjective quality without compromising standard compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video encoding techniques are used to maximize bitrate efficiency, then compression ratio is improved, but subjective video quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating encoding precision across different regions of the video frame. Human regions of interest (such as faces and skin tones) are encoded with higher precision and lower quantization parameters, while background regions use lower precision with higher quantization parameters. This regional differentiation maintains high subjective quality in critical areas while achieving efficient compression overall.
Solution Approach 2:
The patent dynamically adjusts encoding parameters including quantization parameters (QP), lambda factors, and bit allocation based on psychovisual sensitivity maps and content analysis. By changing these parameters adaptively across different regions and time periods, the system optimizes the balance between bitrate efficiency and perceived video quality according to human visual system characteristics.
2Manufacturing precision
If adaptive quantization with psychovisual models is implemented, then subjective video quality is improved, but encoder complexity increases
Solution Approach 1:
The patent performs preliminary psychovisual analysis and content characterization before the actual encoding process. By pre-computing psychovisual sensitivity maps, human region detection, and content-type classification in advance, the system prepares encoding parameters ahead of time, reducing the computational burden during real-time encoding while maintaining high subjective quality.
Solution Approach 2:
The encoder utilizes built-in analysis tools to automatically detect human regions, assess psychovisual sensitivity, and determine optimal encoding parameters without external intervention. The system self-adjusts quantization parameters and bit allocation based on its own content analysis, reducing the need for complex external control mechanisms while achieving quality optimization.
3Manufacturing precision
If human region of interest detection is applied, then quality in critical regions is improved, but processing time increases
Solution Approach 1:
The patent segments the video frame into distinct regions based on content type and human presence. By dividing the image into regions of interest (containing human faces, skin tones) and non-interest regions, the system can apply different encoding strategies to each segment. This segmentation allows targeted quality enhancement in critical areas while maintaining faster processing through parallel encoding of different regions.
Data Source
AI summary
Techniques related to improved video encoding including content, psychovisual, region of interest, and persistence based adaptive quantization are discussed. Such techniques may include generating block level rate distortion optimization Lagrange multipliers and block level quantization parameters for blocks of a picture to be encoded and determining coding parameters for the blocks based on a rate distortion optimization using the Lagrange multipliers and quantization parameters.