Entropy Coding Transform Coefficients With Bin Budget Constraints
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Solution Overview
Problem
High-efficiency video coding (HEVC) faces challenges in efficiently entropy coding transform coefficients, particularly in managing the number of regular bins used for transform blocks, which affects bitstream parsing throughput and coding efficiency.
Innovation Solution
Implementing a method where the number of regular bins used for entropy coding transform blocks is limited by a regular bin budget, allowing a switch to bypass mode when the budget is reached, regardless of whether the blocks represent residual signals or transform coefficients, to ensure consistent coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If regular bins with context modeling are used for entropy coding transform blocks, then coding efficiency is improved, but bitstream parsing throughput is reduced
Solution Approach 1:
The patent dynamically switches between regular bin mode and bypass bin mode based on the regular bin budget status. When the budget is available, regular bins with context modeling are used for better coding efficiency. When the budget is exhausted, the system transitions to bypass bins without context modeling to maintain throughput, creating a dynamic adaptation to resolving the contradiction between coding efficiency and parsing throughput.
Solution Approach 2:
The patent changes the coding parameter (bin type) based on the regular bin budget constraint. By monitoring the number of regular bins used and comparing it against the budget, the system adjusts the coding approach - using context modeling when within budget and switching to no context modeling when budget is reached, thereby balancing coding efficiency and parsing throughput.
2Measurement precision
If context modeling is applied to all transform blocks, then coding precision is improved, but computational overhead increases
Solution Approach 1:
The patent applies context modeling selectively rather than uniformly across all transform blocks. By using regular bins with context modeling only when the regular bin budget allows, and switching to bypass bins without context modeling when the budget is exhausted, the system provides local quality adaptation - applying complex processing where resources permit and simpler processing where they don't, thus balancing precision and computational overhead.
Solution Approach 2:
The patent implements partial application of context modeling by limiting the number of regular bins that can be used within a transform block based on the regular bin budget. Instead of applying context modeling to all possible bins, the system applies it partially - to the extent allowed by the budget - thereby reducing computational overhead while maintaining precision where most beneficial.
3Loss of information
If the number of regular bins is increased for better coding efficiency, then entropy coding performance is improved, but bitstream throughput is reduced
Solution Approach 1:
The patent implements a feedback mechanism by monitoring the number of regular bins used during entropy coding and comparing it against the regular bin budget. This feedback controls the switching between regular bins and bypass bins - when the budget is approaching exhaustion, the system transitions to bypass bins to maintain throughput, thus using feedback to balance coding performance and bitstream throughput.
Solution Approach 2:
The patent preemptively switches from regular bins to bypass bins when the regular bin budget is reached, before throughput degradation occurs. This preliminary anti-action prevents the potential harm of excessive regular bin usage on bitstream throughput by proactively changing the coding mode at the budget boundary, thereby maintaining overall system performance.
Data Source
AI summary
A video decoder receives data from a bitstream for a block of pixels to be decoded as a current block of a current picture of a video. The current block includes a transform block. The video decoder determines a regular bin budget for the transform block. The video decoder entropy decodes the transform block as either regular bins using context modeling or as bypass bins without context modeling. A number of the regular bins used for entropy coding the transform block is limited by the determined regular bin budget regardless of whether the regular bins represent samples of (i) residual signals used to reconstruct pixel data of the current block or (ii) transform coefficients generated by transform operations of the residual signals. The video decoder reconstructs the current block based on the entropy decoded transform block.