Video Coding Context Model Selection for Intra Prediction
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently encoding and decoding video data due to the high redundancy in video signals, particularly in representing intra prediction directions and motion vectors, which affects compression ratios and bandwidth/storage requirements.
Innovation Solution
The implementation of a method for video encoding and decoding that uses local illumination compensation (LIC) and context modeling to determine the appropriate context model for coding syntax elements, allowing for the selection of the best context model based on neighboring block conditions, thereby optimizing the representation of intra prediction directions and motion vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional video coding techniques are used to encode video data, then the encoding process is simple, but the compression ratio is low and bandwidth/storage requirements are high
Solution Approach 1:
The patent segments the video encoding process into multiple independent context models (e.g., context model 0, context model 1, context model 2) that can be selectively applied to different syntax elements. This segmentation allows the system to process different parts of the video data with appropriate complexity levels, improving overall compression without uniformly increasing complexity across all encoding operations.
Solution Approach 2:
The patent implements dynamic context model selection where the encoder chooses between multiple context models based on the specific syntax element being encoded and neighboring block characteristics. This dynamic adaptation allows the system to optimize compression ratios by selecting the most appropriate context model for each encoding decision, rather than using a static, uniform approach.
2Productivity
If context modeling is used to select the best context model for coding syntax elements, then coding efficiency is improved, but the complexity of the encoding/decoding process increases
Solution Approach 1:
The patent applies different context models to different local regions or syntax elements based on their specific characteristics. For example, certain context models are applied to intra prediction direction syntax elements while others are applied to motion vector syntax elements. This local differentiation improves coding efficiency by matching the context model to the local statistical properties of each syntax element type.
Solution Approach 2:
The patent changes the parameters of the context models (such as probability values and scaling factors) based on neighboring block conditions and syntax element types. This parameter adaptation allows the same context model structure to be reused with different parameters for different encoding scenarios, improving efficiency without requiring completely separate models for each case.
3Measurement precision
If multiple context models are used to represent intra prediction directions, then the accuracy of representation is improved, but the bit requirements increase
Solution Approach 1:
The patent applies multiple context models selectively rather than using all available context models for every syntax element. The system uses criteria such as neighboring block prediction modes and syntax element types to determine when to apply enhanced context modeling. This partial application maintains high representation accuracy for critical syntax elements while avoiding unnecessary bit overhead for less critical elements.
Solution Approach 2:
The patent combines multiple context models into a composite encoding approach where different context models work together to represent different aspects of the syntax elements. For example, one context model may represent the primary prediction direction while another represents alternative directions or refinement information. This composite approach achieves high representation accuracy through the synergistic combination of multiple models rather than relying on a single complex model.
Data Source
AI summary
Aspects of the disclosure provide a method and an apparatus for video coding. In some examples, an apparatus includes processing circuitry that receives a bit stream that includes coded information representing a bin of a syntax element for a current block in a picture. The bin of the syntax element indicates whether a local illumination compensation (LIC) process is applied to code the current block. The processing circuitry selects a context model from multiple candidate context models applicable to coding of the syntax element, according to at least a coding characteristic of a region neighboring the current block, and decodes the coded information according to the selected context model to obtain the bin of the syntax element. The processing circuitry reconstructs the current block in response to the bin of the syntax element.