Image Decoding PROF Application via RPR Conditions
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Solution Overview
Problem
There is a need for high-efficient image compression technology to effectively transmit, store, and reproduce high-resolution and high-quality images, as the increased resolution and quality of image data lead to a significant increase in the amount of transmitted and stored information, resulting in higher costs.
Innovation Solution
An image encoding/decoding method and apparatus that performs prediction refinement with optical flow (PROF), which involves deriving a prediction sample of a current block based on motion information, determining a reference picture resampling condition, and applying PROF to refine the prediction sample, while considering the sizes of the current and reference pictures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image resolution and quality are improved, then image quality is improved, but the amount of transmitted information increases
Solution Approach 1:
The patent extracts and transmits only the essential information needed to represent the image by using predictive coding. Instead of transmitting all pixel data, the system transmits motion information and residual data, separating the redundant information from the essential information.
Solution Approach 2:
The patent performs prediction operations before transmission by generating prediction samples based on motion information and reference pictures. This preliminary action allows the actual image data to be compressed by only transmitting the difference between the predicted and actual samples.
2Manufacturing precision
If the amount of transmitted information increases, then image quality is improved, but transmission cost increases
Solution Approach 1:
The patent changes the representation parameters from raw pixel data to motion-compensated prediction residuals. By transforming the data into a different parameter space (motion vectors, prediction modes, residuals), the system achieves better compression efficiency while maintaining image quality.
3Productivity
If prediction refinement with optical flow is applied, then encoding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies optical flow-based prediction refinement selectively rather than universally. The system determines whether PROF should be applied based on picture size and other conditions, performing the complex operation only when beneficial, thus balancing encoding efficiency improvement against computational complexity.
Data Source
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 comprises deriving a prediction sample of a current block based on motion information of the current block, deriving a reference picture resampling (RPR) condition for the current block, determining whether prediction refinement with optical flow (PROF) applies to the current block based on the RPR condition, and deriving a refined prediction sample for the current block by applying PROF to the current block.