Adaptive Video Coding with HVS QP Mapping for Stable Bitrate
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Solution Overview
Problem
Existing video coding technologies, such as the HEVC test model, do not effectively adapt to the human visual system (HVS), resulting in inefficient bit allocation and varying quality across different video scenes, often requiring high target bitrates for maintaining acceptable quality.
Innovation Solution
Implement a target rate adaptive visual mask calculation that considers spatial and temporal complexity, using HVS analysis to adjust quantization parameters (QP) for each block of a frame, integrating history complexity to optimize bit allocation and improve quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed target bitrate is used for video encoding, then the bit rate is predictable and easy to control, but the video quality varies across different scenes with complex or simple content
Solution Approach 1:
The patent applies local quality by using visual masks to identify different regions in the video frame (e.g., foreground objects, background, high-motion areas) and applying different quantization parameters to each region. This allows the encoder to maintain high quality in important regions while using lower quality in less important regions, achieving both quality consistency and bit rate predictability
Solution Approach 2:
The patent uses dynamic visual masks that adapt to each frame's content characteristics, such as motion detection and complexity analysis. The quantization parameters are dynamically adjusted based on the detected visual importance of different regions, allowing the system to respond to varying scene complexity while maintaining overall bit rate control
2Manufacturing precision
If more bits are allocated to maintain quality in complex scenes, then video quality improves, but the bit rate increases and storage requirements grow
Solution Approach 1:
By applying different quality levels to different regions through visual masks, the patent avoids uniformly high quality across the entire frame. Important regions receive higher quality encoding while less important regions use lower quality, reducing overall bit rate while maintaining perceived video quality
Solution Approach 2:
The patent changes the quantization parameter based on the visual mask classification and scene complexity. By dynamically adjusting QP values according to regional importance rather than using a fixed QP, the system achieves better quality at lower bit rates
3Productivity
If visual masks are used to adapt to human visual system, then bit allocation efficiency improves, but the encoding complexity increases
Solution Approach 1:
The patent segments the video frame into different regions using visual masks based on motion detection and complexity analysis. This segmentation allows independent processing of different regions with appropriate quality levels, improving bit allocation efficiency while keeping the complexity manageable through systematic region classification
Data Source
AI summary
An example apparatus for encoding video frames includes a mask selector to select a subset of visual masks according to an actual target compression ratio and GOP configuration and a complexity estimator to estimate a picture level spatial/temporal complexity for a current frame. The example apparatus further includes a GOP adaptive visual mask selector to specify a visual mask from the subset of the visual masks corresponding to the estimated spatial and temporal complexity value a good enough picture QP deriver to derive a good enough picture QP value using the visual mask. The example apparatus also includes an adjustor to adjust the good enough picture QP value based on block level human visual system sensitivity and statistics of already encoded frames to obtain a final human visual system QP map.