CCALF and ALF Coefficient Fusion for Efficient Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency, enhancing image quality, and reducing circuit scale and processing resources, particularly in the context of cross component adaptive loop filtering processes.
Innovation Solution
Implementing a cross component adaptive loop filtering (CCALF) process that clips coefficient values for both luma and chroma components, followed by adding them to enhance image quality and efficiency, while utilizing a block splitter, intra and inter predictors, loop filters, transformers, quantizers, and entropy encoders/decoders to optimize video coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If CCALF and ALF processes are applied to enhance image quality, then image quality improves, but processing resource utilization increases
Solution Approach 1:
The patent applies partial action by selectively processing only chroma components that benefit from CCALF, rather than all chroma samples. The filter is applied conditionally based on whether chroma loop filter is enabled, and only to specific chroma samples where luma and chroma coefficients are both available, reducing unnecessary processing while maintaining image quality where it matters most.
2Device complexity
If coefficient clipping is applied to reduce complexity, then device complexity reduces, but manufacturing precision deteriorates
Solution Approach 1:
The patent changes the parameter range of filter coefficients by applying clipping to constrain coefficients within a specific range (e.g., -3 to 3). This parameter transformation simplifies the device complexity by limiting the search space and computational requirements while maintaining sufficient filtering precision through the use of multiple coefficient values and adaptive selection.
3Productivity
If cross component filtering is implemented to improve coding efficiency, then coding efficiency improves, but device complexity increases
Solution Approach 1:
The patent merges the CCALF process with the existing ALF framework, combining luma and chroma filtering operations into a unified process. The circuit uses the same loop filter structure for both luma and chroma components, with chroma filtering being an extension of the luma filtering capability, thereby reducing overall device complexity while achieving cross-component filtering benefits.
4Manufacturing precision
If multiple filter coefficients are calculated and added, then image quality improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing luma and chroma filter coefficients before the actual filtering operation. The coefficients are computed in advance based on gradient calculations and stored for reuse, allowing the final filtering to be performed more quickly by simply retrieving and combining pre-computed values rather than calculating everything in real-time.
Data Source
AI summary
An encoder includes circuitry and memory. The circuitry, in operation, generates a first coefficient value by applying a CCALF (cross component adaptive loop filtering) process to a first reconstructed image sample of a luma component. The circuitry generates a second coefficient value by applying an ALF (adaptive loop filtering) process to a second reconstructed image sample of a chroma component. The circuitry generates a third coefficient value by adding the first coefficient value to the second coefficient value, and encodes a third reconstructed image sample of the chroma component using the third coefficient value. In the CCALF process, in response to a coordinate of the second reconstructed image sample being (x, y), coordinates of the first reconstructed image samples are (2x, 2y−1), (2x−1, 2y), (2x, 2y), (2x+1, 2y), (2x−1, 2y+1), (2x, 2y+1), (2x+1, 2y+1), and (2x, 2y+2).