Slice Context Initialization for Parallel Entropy Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
State-of-the-art video-coding methods like H.264/AVC and TMuC provide higher coding efficiency but at the expense of increased complexity, which can be exacerbated by higher quality and resolution requirements, with entropy decoding often becoming a bottleneck due to serial processing within slices.
Innovation Solution
The method involves partitioning reconstruction slices into multiple entropy slices, allowing parallel entropy decoding by initializing context models at the start of each entropy slice and decoupling coefficient scanning from context fetch order, with forward-predicted B-slices using P-slice methods and context initialization based on bin count or quantization parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher quality and resolution requirements are imposed on video coding methods, then coding efficiency is improved, but complexity increases
Solution Approach 1:
The patent divides a picture into multiple slices, where each slice can be decoded independently in parallel. This segmentation allows the decoder to process multiple slices simultaneously using multi-core processors, thereby reducing the overall decoding complexity while maintaining high coding efficiency for high-quality and high-resolution video content.
2Reliability
If serial processing within slices is used for entropy decoding, then decoding accuracy is maintained, but decoding speed decreases
Solution Approach 1:
By dividing the picture into multiple independent slices, the patent enables parallel entropy decoding of different slices. Each slice maintains its own context model state, ensuring decoding accuracy within each slice while allowing simultaneous processing of multiple slices to improve overall decoding speed.
Solution Approach 2:
The patent initializes context model states at the beginning of each slice before processing. This preliminary initialization ensures that each slice starts with a clean state, maintaining decoding accuracy while enabling independent parallel processing of multiple slices to improve decoding speed.
3Productivity
If parallel decoding is implemented, then decoding speed is improved, but memory requirements increase
Solution Approach 1:
The patent segments the video data into multiple slices that can be decoded in parallel. Each slice uses its own context model state, which is initialized at the start of the slice. This segmentation allows parallel processing to improve decoding speed while managing memory requirements by limiting the context state to each individual slice rather than maintaining a single large state for the entire picture.
Data Source
AI summary
Technique for initialization of encoders and decoders. In some cases, the decoder receives a slice and identifies if the slice is either a forward predicted B-slice or a backward predicted B-slice, and not both a forward and backward predicted B-slice, and based upon this identification initializes, using a P-slice technique, a context associated with the slice.