Video Coding Artifact Reduction via Slice Recoding
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Solution Overview
Problem
Video compression techniques often result in coding artifacts due to quantization, which can lead to unpredictable compression ratios and significant information loss, making it difficult to predict the performance of the compression process and efficiently use computational resources.
Innovation Solution
A method that partitions video frames into pixel blocks, estimates coding artifacts in slices, and revises coding parameters to recode slices with high artifact likelihood, while maintaining a target frame size, thereby reducing coding artifacts and optimizing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization is applied to compress video data, then compression ratio is improved, but coding artifacts and information loss increase
Solution Approach 1:
The patent applies preliminary action by performing an initial coding pass to identify regions with high artifact likelihood before final coding. The system estimates coding artifacts in slices during a first pass, then uses this information to guide quantization parameter selection in a second pass, preventing information loss in critical regions before it occurs.
Solution Approach 2:
The patent implements local quality by applying different quantization strategies to different regions of the video frame. Slices with high estimated artifact likelihood receive different treatment (such as lower quantization levels or additional refinement) compared to slices with low artifact likelihood, ensuring that important regions maintain higher quality while less important regions achieve better compression.
2Productivity
If all permutations of quantization parameters are tested, then optimal compression is achieved, but computational resources are wasted
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters based on estimated coding artifacts rather than testing all permutations. The system changes quantization parameters selectively for slices with high artifact likelihood, avoiding exhaustive search in regions where it would be wasteful while maintaining optimization where needed.
Solution Approach 2:
The patent uses preliminary artifact estimation to guide subsequent coding decisions, avoiding the need to test all quantization parameter permutations. The first pass identifies problematic regions, allowing the second pass to focus computational resources only on those regions, significantly reducing overall computational effort.
3Loss of information
If quantization parameters are reduced to improve quality, then coding artifacts are reduced, but compression ratio deteriorates
Solution Approach 1:
The patent implements local quality by applying different quantization parameters to different slices based on their estimated artifact likelihood. Slices with high artifact likelihood use lower quantization parameters (better quality, lower compression), while slices with low artifact likelihood use higher quantization parameters (worse quality, better compression), achieving overall optimization.
Solution Approach 2:
The patent dynamically changes quantization parameters based on content characteristics and estimated artifact likelihood. Rather than using uniform parameters across the entire frame, the system adjusts parameters locally for each slice, allowing quality improvements where needed while maintaining compression efficiency elsewhere.
Data Source
AI summary
Techniques for reducing reduce coding artifacts in video data are disclosed. In one aspect, a frame of video data is partitioned into pixel blocks, which are organized into slices. The pixel blocks of each slice are coded by a compression algorithm and an estimate of coding artifacts in the slice is made. For slices that are estimated to possess coding artifacts, the method revises coding parameters applied to pixel blocks in the slice and recodes the slice. The method substitutes recoded slices for originally-coded slices in frame, working in a priority order from a slice with the highest estimated likelihood of coding artifacts down to slices with lower estimated likelihoods of coding artifacts, measuring changes in the frame's coding size as it goes. The likelihood of coding artifacts can be estimated from slice statistics that may be developed from a comparison of transform coefficients among the pixel blocks within a slice, from an evaluation of transform coefficients of a pixel block with a slice that is estimated to have a lowest spatial complexity, or from coded luma data of the pixel blocks within a slice. In a further aspect, slice statistics may be computed from pixel block data only for a subset of slices within a frame. Slice statistics for other slices may be derived from the statistics of neighboring slices. In another aspect, a method may revise coding parameters in iterative fashion working from an initialized value and estimate (without recoding them) data sizes of coded slices that may be obtained from the revised parameters. As the method operates, it may compare the coding parameters to parameters used in a first iteration of coding and terminate the iterative process for that slice if the first iteration coding parameters are higher than the revised parameter.