BDOF Image Decoding With Gradient-Based Motion Refinement
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to a significant increase in transmission and storage costs due to the rise in the amount of transmitted information, necessitating high-efficient image compression technologies.
Innovation Solution
An image encoding/decoding apparatus that performs bi-directional optical flow (BDOF) to derive a gradient and BDOF offset, enhancing encoding/decoding efficiency and enabling the transmission of a bitstream generated by the encoding apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images are transmitted, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent changes the parameter of gradient calculation by applying right-shifting operations with different shift amounts (first shift and second shift) to the prediction samples. This parameter change allows the system to work with reduced precision intermediate values while maintaining final output quality, thereby reducing the amount of transmitted information without sacrificing image quality.
2Device complexity
If conventional BDOF gradient calculation is used, then processing is simpler, but encoding/decoding efficiency is lower
Solution Approach 1:
The patent performs preliminary right-shifting operations on the prediction samples before gradient calculation. By pre-processing the data with appropriate shift amounts, the system prepares optimized input values that enable more efficient subsequent processing, thereby improving encoding/decoding efficiency while maintaining manageable processing complexity.
Data Source
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AI summary
An image encoding/decoding method and apparatus are provided. An image decoding method according to the present disclosure is performed by an image decoding apparatus. The image decoding method may comprise deriving a prediction sample of a current block based on motion information of the current block, determining whether bi-directional optical flow (BDOF) applies to the current block, based on that the BDOF applies to the current block, deriving a gradient for a current subblock in the current block, deriving motion refinement (vx, vy) for the current subblock based on the gradient, deriving a BDOF offset based on the gradient and the motion refinement, and deriving a refined prediction sample for the current block based on the prediction sample of the current block and the BDOF offset.