Arithmetic Coding Context Initialization Using Reference Frame Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding methods face challenges in achieving accurate entropy coding with efficient probability models, leading to suboptimal compression and increased signaling overhead.
Innovation Solution
Implementing a weighted average of probability models from multiple reference frames for initializing the context of a current frame, reducing signaling overhead and improving coding accuracy by using a more accurate context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If default initialization values are used for probability models, then device complexity is reduced, but coding accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing probability models from reference frames before decoding the current frame. The decoder retrieves and applies these pre-computed models to initialize entropy decoding contexts, avoiding the need for complex real-time probability computations during decoding while maintaining high coding accuracy.
2Measurement precision
If probability models from multiple reference frames are computed and applied, then coding accuracy is improved, but signaling overhead increases
Solution Approach 1:
The patent uses copying by replicating and applying probability models from reference frames to the current frame without transmitting additional signaling data. The decoder copies the context initialization approach from reference frame decoding, using stored probability models to initialize entropy decoding, thereby avoiding extra signaling overhead while improving coding accuracy through multi-reference-frame utilization.
3Measurement precision
If probability models from multiple reference frames are computed and applied, then coding accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies merging by combining probability models from multiple reference frames through weighted averaging to create an optimized initialization model for the current frame. The encoder and decoder merge the same reference frame models using identical weighting schemes, ensuring synchronization without complex communication protocols, thereby improving coding accuracy while managing device complexity through symmetric processing.
Data Source
AI summary
An example method of video decoding includes receiving a video bitstream comprising a plurality of frames and identifying a set of reference frames for a current frame of the plurality of frames. When the set of reference frames includes more than one frame, the method also includes obtaining respective contexts for the set of reference frames and initializing a current frame context for the current frame by performing a weighted average of the respective contexts. When the set of reference frames includes only one frame, the method also includes initializing the current frame context for the current frame based on a context of the one frame. The method further includes entropy decoding one or more syntax elements for the current frame using the current frame context and reconstructing the current frame using information of the one or more syntax elements.