Video Decoding Filter Blending for Block Noise and Edge Preservation
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Solution Overview
Problem
Existing video coding and decoding technologies face issues with image smoothing leading to loss of edge information when using deblocking filters, and neural network filters fail to adequately handle different transfer functions and color spaces, resulting in visible block noise when strength is set to zero.
Innovation Solution
A video decoding apparatus with a parameter decoder, bS derivation unit, DF unit, and NN filter unit, along with image combining, performs filter processing based on luma and chroma parameters to reduce block noise across various transfer functions and color spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the deblocking filter is applied using a bS value indicating strength of the deblocking filter, then block noise is reduced, but the image is smoothed and edge information is lost
Solution Approach 1:
The patent implements dynamic switching between deblocking filter processing and neural network filter processing based on region characteristics. The processing type is determined dynamically for each block using nn_area_flag, allowing the system to adaptively select the appropriate filter method rather than applying a fixed filtering approach throughout the entire image.
Solution Approach 2:
The patent applies different filtering methods to different regions of the image based on their characteristics. By dividing the image into blocks and independently determining the processing type for each block using nn_area_flag, the system ensures that edge regions receive appropriate treatment to preserve edge information while non-edge regions receive deblocking filtering to reduce block noise.
2Device complexity
If the scaling value of the neural network filter strength is set equal to 0, then computational complexity is reduced, but block noise becomes visible
Solution Approach 1:
The system dynamically adjusts the neural network filter strength scaling value based on the processing type determined by nn_area_flag. When neural network filtering is selected for a block, the scaling value is set to apply appropriate strength; when deblocking filtering is selected, the neural network filter is bypassed. This dynamic adjustment allows the system to reduce computational complexity in regions where strong neural network filtering is not needed while maintaining block noise reduction where it is beneficial.
3Object-affected harmful factors
If the neural network filter is applied to images with multiple transfer functions and color spaces, then filtering effectiveness may improve, but processing appropriateness deteriorates due to lack of proper handling for different transfer functions and color spaces
Solution Approach 1:
The patent applies different filtering methods (deblocking filter versus neural network filter) to different regions based on their characteristics. By determining the processing type independently for each block using nn_area_flag, the system ensures that regions with different transfer functions and color spaces receive filtering treatment appropriate to their specific characteristics, thereby maintaining processing appropriateness across diverse image regions.
Data Source
AI summary
In a case that a scaling value of neural network filter strength is set equal to 0, a decoded image before a deblocking filter is output, and thus block noise occurs. In a case that luma and chroma parameters are not taken into consideration, processing for an image of multiple transfer functions and chroma parameters is not appropriately performed. In a unit of a prescribed block, by using a parameter indicating a degree of application of the NN filter, an image after the deblocking filter and an image after an NN filter are combined using a different ratio of the images. Filter processing of a luma image is switched based on a luma parameter, and filter processing of a chroma image is switched based on a chroma parameter.