Context-Based Adaptive Probability Model for Video Block Compression
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Solution Overview
Problem
The existing encoding scheme for the index of an extra reference frame in the Audio Video coding Standard (AVS) has a high bit rate due to the use of an equi-probability model for encoding the extra reference frame.
Innovation Solution
An arithmetic encoding-decoding method and codec that utilize a context-based adaptive probability model to encode and decode the index of the reference frame, reducing the bit rate by better reflecting the probability distribution of the index, which involves acquiring and updating the context-based adaptive probability model during both encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If an equi-probability model is used to encode the index of the extra reference frame, then the encoding process is simple, but the bit rate is high
Solution Approach 1:
The patent changes the encoding parameter from an equi-probability model to a context-based adaptive probability model. This model dynamically adjusts probability estimates based on contextual information from previously decoded symbols, allowing more efficient encoding of the reference frame index by assigning shorter codewords to more frequent symbols and longer codewords to less frequent symbols, thereby reducing the bit rate while maintaining encoding feasibility
Solution Approach 2:
The patent introduces a dynamic probability model that adapts during the encoding process. The context-based adaptive probability model updates its probability estimates as encoding progresses, making the encoding scheme dynamic rather than static. This allows the encoder to respond to the actual data being encoded and optimize the bit rate accordingly, resolving the contradiction between simplicity and efficiency
2Quantity of substance
If a context-based adaptive probability model is used to encode the index of the reference frame, then the bit rate is reduced, but the encoding process becomes more complex
Solution Approach 1:
The context-based adaptive probability model employs feedback mechanisms where the encoder uses information from previously decoded symbols to update probability estimates for subsequent symbols. This feedback loop allows the model to adapt to the actual statistical properties of the data being encoded, achieving better compression ratios. The feedback mechanism is implemented through context updating rules that modify probability estimates based on observed symbol sequences, resolving the complexity-benefit tradeoff by making the complexity worthwhile through significant bit rate reduction
3Ease of manufacture
If the equi-probability model is used for encoding, then the implementation is straightforward, but the compression efficiency is low
Solution Approach 1:
The patent transforms the encoding approach by changing from a static equi-probability parameter to a dynamic context-based adaptive probability parameter. This parameter change enables the encoder to adapt to the actual data characteristics, significantly improving compression efficiency. The implementation complexity increase is justified by the substantial gains in compression performance, as the adaptive model can achieve better compression ratios by exploiting contextual dependencies in the reference frame index data
Data Source
AI summary
An arithmetic encoding-decoding method for compression of a video image block. The method includes an encoding process and a decoding process. The encoding process includes: 1) acquiring an information of an image block to be encoded; 2) extracting an encoding command of a weighted skip model; 3) acquiring an index of a reference frame according to the information of the image block to be encoded and the command of the weighted skip model, in which the reference frame includes a prediction block for reconstructing the image block to be encoded; 4) acquiring a context-based adaptive probability model for encoding; and 5) performing arithmetic encoding of the index of the reference frame and writing arithmetic codes into an arithmetically encoded bitstream according to the context-based adaptive probability model for encoding.