ResNet In-Loop Filtering with Spatial Attention for Video Coding
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Solution Overview
Problem
Existing video coding techniques face challenges in effectively reducing video artifacts and improving filtering performance, particularly due to the complexity and resource-intensive nature of neural network-based filtering methods.
Innovation Solution
Integration of a spatial attention mechanism into a ResNet-based in-loop filtering architecture, utilizing residual blocks and attention blocks to enhance filtering performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network-based filtering methods are used to reduce video artifacts, then filtering performance is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The neural network filter is divided into multiple residual blocks, each processing a specific portion of the video data. This segmentation allows the complex filtering task to be broken down into manageable stages, reducing the computational burden on any single processing unit while maintaining overall filtering effectiveness.
Solution Approach 2:
The patent applies different processing intensities to different regions of the video data through the residual blocks. Each block can focus on specific artifacts or regions requiring attention, allowing the system to allocate computational resources more efficiently rather than uniformly processing the entire video frame.
2Reliability
If neural network-based filtering methods are used to reduce video artifacts, then filtering performance is improved, but processing time increases
Solution Approach 1:
By dividing the filtering process into sequential residual blocks that process different portions of the video data, the system can parallelize operations across multiple blocks. This segmentation enables simultaneous processing of multiple regions, significantly reducing total processing time while maintaining high filtering performance.
Solution Approach 2:
The residual blocks are designed to process video data continuously through the pipeline rather than sequentially waiting for completion. This continuous processing approach ensures that filtering operations are performed efficiently without idle time, reducing overall processing duration while maintaining artifact reduction effectiveness.
3Reliability
If ResNet-based in-loop filtering architecture is used, then filtering effectiveness is improved, but device resource consumption increases
Solution Approach 1:
The ResNet architecture is segmented into multiple residual blocks that can be processed in parallel across different hardware units. This segmentation allows the system to distribute computational workload across multiple cores or processing units, reducing the resource consumption burden on any single device while maintaining filtering effectiveness.
Solution Approach 2:
The patent implements residual blocks that process only the necessary portions of the video data rather than the entire frame. This partial processing approach reduces the total computational workload and resource consumption while still achieving effective artifact reduction in the critical regions where it is most needed.
Data Source
AI summary
Example methods and devices are described for processing video data. An example device includes one or more memories configured to store a reconstructed block of the video data and one or more processors in communication with the one or more memories. The one or more processors are configured to receive encoded video data, the encoded video data representing a block of the video data. The one or more processors are configured to reconstruct the block based on the encoded video data to generate the reconstructed block. The one or more processors are configured to perform a neural network (NN)-based filter process on the reconstructed block to generate a filtered block, wherein as part of performing the NN-based filter process, the one or more processors are configured to apply one or more residual groups to a residual group input, each of the residual groups comprising a respective attention block.