Video In-Loop Filtering with Residual Approximation for Coding Efficiency
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Solution Overview
Problem
Existing video compression techniques face challenges in achieving high coding efficiency and image enhancement due to increasing image sizes, resolutions, and frame rates, necessitating improved methods to reduce computation and enhance video quality.
Innovation Solution
A video coding method and apparatus that utilize a deep learning-based model to generate a residual frame from a reconstructed frame, followed by a linear model to approximate the original frame, thereby enhancing the in-loop filter performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based image processing technology is applied to in-loop filter, then video quality is improved, but computation amount increases
Solution Approach 1:
The patent introduces a residual frame generator that creates a residual frame representing the difference between the reconstructed frame and the original frame. This residual frame serves as an intermediary that captures essential information needed for quality improvement while being more compact and easier to process than the full-resolution reconstructed frame, thus reducing computation amount while maintaining video quality improvement benefits
Solution Approach 2:
The patent extracts only the necessary information for quality improvement by generating a residual frame that contains only the difference between the reconstructed and original frames. This extraction approach allows the system to focus computational resources on processing only the relevant information needed for enhancement, rather than processing the entire high-resolution frame, thereby reducing computation amount while preserving video quality improvement
2Manufacturing precision
If image size, resolution, and frame rate increase, then video quality is improved, but data amount increases
Solution Approach 1:
The patent creates a residual frame that is a simplified copy or representation of the difference between the reconstructed and original frames. This residual copy contains essential quality information in a compressed form, allowing the system to work with reduced data amounts while still achieving video quality improvement through subsequent processing of this compact residual information
Data Source
AI summary
A method and an apparatus are disclosed for video coding using an improved in-loop filter. The video coding method and the apparatus generate a residual frame from a reconstructed frame using a deep learning model. The video coding method and the apparatus improve performance of an in-loop filter by approximating an original residual frame by applying the generated residual frame to a linear model.