Image Coding Syntax for Affine and Sub-Block Motion Prediction
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and immersive media requires more efficient image/video compression techniques to reduce transmission and storage costs, particularly in the context of high-resolution images and emerging formats like VR and AR.
Innovation Solution
A syntax design method and apparatus that utilizes high-level and low-level syntax elements for motion prediction based on sub-blocks and affine models, including the use of affine flags and sub-block TMVP flags to determine the application of merge modes in image coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional compression techniques are used for high-resolution images, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The current block is divided into multiple sub-blocks for independent motion prediction. Each sub-block can have its own motion vector and prediction mode, allowing more precise representation of local motion patterns in high-resolution images, thereby improving compression efficiency without sacrificing image quality
Solution Approach 2:
The patent implements dynamic motion prediction by selecting different prediction modes (affine, merge, skip) based on block characteristics and motion complexity. The system adapts the prediction strategy to local content requirements, improving compression performance across varying image qualities and resolutions
2Productivity
If complex motion prediction models are used, then coding efficiency improves, but computational complexity and processing time increase
Solution Approach 1:
The system dynamically selects prediction modes based on block characteristics, motion complexity, and available reference blocks. Simple blocks use efficient merge or skip modes, while complex blocks use affine or sub-block prediction, optimizing the balance between coding efficiency and computational complexity
Solution Approach 2:
Different prediction modes and complexities are applied to different regions of the image based on local motion characteristics. High-motion regions receive more complex prediction treatment, while low-motion regions use simpler methods, reducing overall computational complexity while maintaining coding efficiency
3Measurement precision
If affine prediction is applied to all blocks, then prediction accuracy improves, but bitstream overhead and processing load increase
Solution Approach 1:
The block is segmented into sub-blocks that can independently use affine prediction when needed. This selective application reduces the overall overhead compared to applying affine prediction to the entire block, while still achieving high prediction accuracy in regions requiring it
Solution Approach 2:
Affine prediction is dynamically enabled or disabled based on block characteristics, motion patterns, and available reference data. The system uses flags and conditional logic to apply affine prediction only when it provides significant accuracy improvement, minimizing bitstream overhead
Data Source
AI summary
An image decoding method performed by a decoding apparatus according to the present disclosure comprises the steps of: decoding, on the basis of a bitstream, an affine flag that indicates whether affine prediction is applicable to a current block and a sub-block TMVP flag that indicates whether a temporal motion vector predictor based on a sub-block of the current block is usable; determining whether to decode a predetermined merge mode flag that indicates whether to apply a predetermined merge mode to the current block, on the basis of the decoded affine flag and the decoded sub-block TMVP flag; deriving prediction samples of the current block on the basis of the determining of whether to decode the predetermined merge mode flag; and generating reconstructed samples of the current block based on the prediction samples of the current block.