Visual Quality Assessment for Affine Video Transform
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently encoding and decoding video sequences, particularly in reducing redundant information and maintaining visual quality during transmission, especially for screen content and natural scenes with complex motions.
Innovation Solution
The implementation of advanced prediction techniques such as intra block copy (IBC) and affine motion field prediction, combined with visual quality assessment-based encoding processes, to enhance compression efficiency and accuracy in video encoding and decoding systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding techniques are used, then encoding complexity is reduced, but compression efficiency and visual quality deteriorate for complex motion patterns
Solution Approach 1:
The patent divides the video encoding process into multiple stages: initial prediction block generation, affine transformation application, visual quality assessment, and selective refinement. This segmentation allows the system to apply complex operations only where needed, improving compression efficiency without uniformly increasing encoding complexity across all blocks.
Solution Approach 2:
The patent implements partial affine transformation refinement based on visual quality assessment results. Instead of applying complex affine transformations to all prediction blocks, the system selectively refines only those blocks that fail visual quality thresholds, performing partial action to achieve better compression efficiency while controlling overall encoding complexity.
2Productivity
If redundant information is reduced through compression, then transmission efficiency improves, but visual quality deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where visual quality assessment results guide the encoding process. The assessment evaluates prediction block quality and provides feedback to determine whether affine transformation refinement should be applied, creating a closed-loop system that maintains visual quality while optimizing transmission efficiency.
Solution Approach 2:
The patent dynamically changes encoding parameters based on visual quality assessment. When assessment indicates poor visual quality, the system applies affine transformation refinement to adjust prediction accuracy. This parameter change strategy maintains visual quality standards while optimizing transmission efficiency by avoiding unnecessary refinement of already high-quality blocks.
3Manufacturing precision
If advanced prediction techniques are implemented, then visual quality is maintained, but encoding complexity increases
Solution Approach 1:
The patent performs preliminary visual quality assessment on prediction blocks before deciding whether to apply affine transformation refinement. This preliminary action allows the system to identify blocks that require refinement in advance, avoiding unnecessary complex operations on blocks that already meet visual quality standards, thus maintaining visual quality while controlling encoding complexity.
Data Source
AI summary
A decoder may receive, for a block and from a bit stream, an indication of a decoder-side affine transform, a prediction mode, and a residual block. The decoder may generate a compensated prediction of the block. For example, the decoder may generate the compensated prediction of the block based on the residual block and the prediction mode. The decoder may generate, based on the indication and for each of a plurality of affine transform parameters, an affine transformation of the compensated prediction. The decoder may determine an affine transform parameter, from the plurality of affine transform parameters, based on a visual quality of each of the affine transformations of the compensated prediction.