Video Bitstream Quality Signaling for Neural Post-Processing
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Solution Overview
Problem
Existing video coding technologies, such as MPEG-2, MPEG-4, HEVC, and VVC, require improvements in coding quality to enhance video processing efficiency and effectiveness.
Innovation Solution
Implementing a neural-network post-processing filter (NNPF) that applies quality information to video units within the bitstream, allowing for improved coding quality through enhanced video processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding technologies (MPEG-2, MPEG-4, HEVC, VVC) are used, then video compression is achieved, but coding quality is insufficient
Solution Approach 1:
The patent replaces conventional mechanical video coding algorithms with a neural network-based post-processing filter (NNPF). The NNPF uses machine learning models to analyze and improve video quality by filtering artifacts and enhancing visual perception, achieving superior coding quality compared to traditional coding standards while maintaining processing effectiveness
Solution Approach 2:
The patent changes the processing parameters by introducing quality information indicators into the bitstream that guide the NNPF operation. The system dynamically adjusts filtering strength and type based on detected video quality metrics, enabling adaptive quality enhancement that resolves the contradiction between coding quality and processing effectiveness
2Manufacturing precision
If neural-network post-processing filter (NNPF) is applied to improve coding quality, then video quality enhancement is achieved, but bitstream complexity increases
Solution Approach 1:
The patent extracts only the essential quality information indicators from the complex NNPF processing and embeds them in the bitstream. Instead of transmitting full neural network parameters, only key quality metrics and filter selection indicators are included, reducing bitstream overhead while maintaining quality enhancement capabilities
Solution Approach 2:
The patent applies quality enhancement locally by processing different video regions with appropriate filtering strength based on their specific quality characteristics. The NNPF analyzes local quality information and applies adaptive filtering only where needed, rather than uniformly processing the entire video stream, thus improving quality without proportionally increasing bitstream complexity
3Adaptability or versatility
If quality information is embedded in bitstream for NNPF application, then adaptive video processing is enabled, but data transmission overhead increases
Solution Approach 1:
The patent implements partial action by embedding quality information indicators only for specific video units or regions where enhancement is most beneficial, rather than for the entire video stream. This selective approach enables adaptive processing where needed while minimizing the additional data overhead in the bitstream
Data Source
AI summary
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: performing a conversion between a video and a bitstream of the video, wherein at least one neural-network post-processing filter (NNPF) is applied on at least one video unit associated with the video, and the bitstream comprises a first indication indicating whether quality information of the at least one video unit associated with the applying of the at least one NNPF is present in the bitstream.