Video Encoder Adaptive Rate Distortion Optimization
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Solution Overview
Problem
Current video coding standards, such as HEVC and VVC, face challenges in reducing bit-rate for high-resolution video content without compromising perceived quality and managing increased computational complexity.
Innovation Solution
An encoder partitions video frames into coding units and prediction units, using adaptive rate distortion optimization to determine optimal coding modes based on human visual system characteristics and bit-rate targets, minimizing distortion in the encoded bitstream while considering inter-dependencies between prediction units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If VVC standard is used for ultra high-definition video coding, then compression efficiency is improved, but computational complexity increases several times compared to HEVC
Solution Approach 1:
The patent applies local quality by differentiating the treatment of prediction units based on their visual importance. The encoder identifies significant prediction units that contribute more to perceived video quality and applies rate distortion optimization specifically to these units, while using simpler encoding for less important units. This selective approach maintains high compression efficiency for visually critical areas while reducing overall computational complexity.
Solution Approach 2:
The patent segments the video frame into multiple prediction units and further divides them into significant and non-significant groups. By partitioning the coding task and applying different optimization strategies to different segments, the system achieves better compression efficiency for important regions without subjecting the entire frame to computationally intensive processing, thus balancing compression performance and complexity.
2Quantity of substance
If bit-rate is reduced for high-resolution video content, then transmission efficiency is improved, but perceived video quality may deteriorate
Solution Approach 1:
The patent ensures that bit-rate reduction does not uniformly degrade quality across the entire frame. Instead, it concentrates bit-rate allocation on significant prediction units that have greater impact on perceived quality, determined by human visual system characteristics. This allows aggressive compression in less important areas while maintaining quality in visually critical regions, achieving lower overall bit-rate without noticeable quality loss.
Solution Approach 2:
The patent incorporates feedback mechanisms where the encoder evaluates the impact of encoding decisions on perceived quality based on human visual system models. This feedback guides the rate distortion optimization process to make informed decisions about where to allocate bits, ensuring that bit-rate reduction strategies preserve perceived quality by protecting visually important information.
3Manufacturing precision
If rate distortion optimization is applied to minimize distortion, then encoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the rate distortion optimization process by applying it selectively only to significant prediction units rather than uniformly across all prediction units in a frame. This segmentation allows the encoder to achieve high encoding accuracy for visually important regions while avoiding the processing overhead of applying full RDO to every unit, thus reducing overall processing time while maintaining encoding quality where it matters most.
Solution Approach 2:
The patent applies partial action by implementing rate distortion optimization for only a subset of prediction units identified as significant, rather than performing exhaustive optimization on all units. This partial application of RDO achieves sufficient encoding accuracy for perceived quality while dramatically reducing the computational burden and processing time associated with full-frame optimization.
Data Source
AI summary
Systems and methods are described herein for processing video. An encoder implementing the systems and methods described herein may receive video data comprising a plurality of frames and may partition each frame of the plurality of frames into a plurality of coding units. The encoder may then partition a coding unit into two or more prediction units. The encoder may determine, based on one or more coding parameters, a target bit rate, and characteristics of a human visual system (HVS), a coding mode for each of the two or more prediction units to minimize distortion in the encoded bitstream. The encoder may then determine a residual signal comprising a difference between each of the two or more prediction units and each of one or more corresponding prediction areas in a previously encoded frame and then generate an encoded bitstream comprising the residual signal.