Video Coder Entropy Coding Throughput Constraints
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Solution Overview
Problem
Current video coding standards, such as HEVC, face challenges in efficiently encoding transform coefficients due to high complexity and bitstream parsing throughput issues, particularly in managing the number of regular bins used for entropy coding.
Innovation Solution
A video coder is designed to selectively entropy encode syntax elements as either regular bins using context modeling or bypass bins without context modeling, with a constraint limiting the total number of regular bins used for entropy coding, allowing for reduced complexity and improved throughput by switching to bypass mode when the bin limit is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If context modeling is used for entropy coding transform coefficients, then compression efficiency is improved, but device complexity and processing time increase
Solution Approach 1:
The transform coefficient block is divided into multiple subblocks, and entropy coding is performed separately for each subblock. This segmentation allows the system to apply context modeling selectively to only those subblocks that benefit from it, rather than applying it uniformly across the entire block, thereby reducing overall complexity while maintaining compression efficiency where needed.
Solution Approach 2:
Instead of applying context modeling to all transform coefficients, the patent applies it partially to only certain subblocks based on specific criteria (such as subblock size, position, or coefficient characteristics). This partial application reduces the computational burden while still achieving compression benefits in the most beneficial regions.
2Loss of energy
If multiple coding passes are used for entropy coding, then compression efficiency is improved, but processing time and throughput are reduced
Solution Approach 1:
The coding process is divided into multiple passes, but each pass operates on segmented subblocks independently. This allows parallel processing of different subblocks within the same pass and enables early termination of passes for subblocks that have been sufficiently coded, reducing total processing time while maintaining compression efficiency.
Solution Approach 2:
The first coding pass performs preliminary entropy coding on all subblocks using simplified methods, identifying significant coefficients and establishing initial compression. Subsequent passes refine the coding only where necessary, avoiding redundant processing and reducing overall processing time compared to applying multiple full passes uniformly.
3Loss of energy
If context modeling is applied to all subblocks, then compression efficiency is maximized, but bitstream parsing throughput decreases
Solution Approach 1:
The transform coefficient block is segmented into subblocks, allowing the system to apply context modeling selectively rather than uniformly. This segmentation enables the bitstream parser to use faster bypass decoding for subblocks where context modeling is not applied, thereby maintaining high parsing throughput while still achieving compression efficiency in subblocks where context modeling is beneficial.
Solution Approach 2:
Different entropy coding strategies are applied to different subblocks based on their local characteristics. Subblocks with higher complexity or specific patterns receive context modeling for better compression, while simpler subblocks use faster bypass decoding. This local differentiation optimizes the balance between compression efficiency and parsing throughput.
4Loss of energy
If the number of regular bins is increased, then compression efficiency is improved, but device complexity and processing load increase
Solution Approach 1:
The transform coefficient block is divided into subblocks, and regular bins are allocated and used selectively for each subblock rather than for the entire block. This segmentation allows the system to control the total number of regular bins consumed while maintaining compression efficiency in subblocks that benefit most from their use.
Solution Approach 2:
The patent dynamically adjusts the number of regular bins used based on subblock characteristics such as size, position, and coefficient distribution. By changing this parameter adaptively rather than using a fixed number, the system optimizes compression efficiency while controlling processing load and device complexity.
Data Source
AI summary
A video coder that constrains the total number of regular bins used for entropy coding syntax elements of a current block is provided. The video coder entropy encodes or decodes the syntax elements selectively as either regular bins using context modeling or as bypass bins without context modeling. A constraint is specified to limit a total number of regular bins used for entropy coding the syntax elements of the current block. There may be no constraint limiting a number of regular bins specific to an individual syntax element of the current block.