Neural Network In-Loop Filter Signaling for Adaptive Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in effectively reducing distortion during compression, particularly in managing the application of neural network filters across different levels of video units, which can impact bandwidth efficiency and image quality.
Innovation Solution
Implementing neural network filters at various levels within the video coding process, including sequence, picture, slice, and subpicture levels, with syntax elements controlling their application, and using context models to adaptively select and apply filters based on specific video unit characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional in-loop filtering methods are used, then the filtering process is simple and fast, but the adaptability to varying video unit characteristics and quality levels is insufficient
Solution Approach 1:
The patent applies different filtering strategies to different regions of the video based on local characteristics. Syntax elements at various levels (sequence, picture, slice, block) enable adaptive selection of filtering parameters tailored to specific video unit characteristics, achieving local optimization rather than uniform filtering across the entire video stream.
Solution Approach 2:
The filtering method transitions from static traditional filters to dynamic neural network-based filters that can adapt their behavior based on input characteristics. The system dynamically selects and adjusts filtering parameters based on quality-level indicators and coding statistics, making the filtering process responsive to varying video content and compression conditions.
2Manufacturing precision
If neural network filter models are applied, then distortion reduction performance is improved, but the computational complexity and processing overhead increase
Solution Approach 1:
The patent segments the video stream into different units (sequences, pictures, slices, blocks) and applies filtering selectively at each level. Syntax elements control which regions receive neural network filtering versus traditional filtering, dividing the computational workload and allowing high-quality filtering only where most beneficial.
Solution Approach 2:
The system changes filtering parameters dynamically based on quality-level indicators and coding statistics. Instead of applying full neural network filtering uniformly, the patent adjusts filtering intensity and type based on local video characteristics, maintaining high distortion reduction where needed while reducing computational overhead in simpler regions.
3Adaptability or versatility
If multiple syntax elements at different levels are used to control NN filter application, then filtering adaptability is enhanced, but the signaling overhead and bitstream complexity increase
Solution Approach 1:
The patent introduces a multi-level hierarchical control structure with syntax elements at sequence, picture, slice, and block levels. This dimensional expansion of control granularity enables fine-tuned adaptability without requiring complete re-signalizing at every level, as higher-level settings can constrain or guide lower-level decisions.
Solution Approach 2:
The syntax elements serve multiple functions: they control not only whether neural network filtering is applied but also which specific filter model is selected and what parameters are used. This multi-functionality reduces the total number of separate control signals needed, as a single syntax element can simultaneously determine filter activation, model selection, and parameter configuration.
Data Source
AI summary
A method implemented by a video coding apparatus includes applying a neural network (NN) filter to an unfiltered sample of a video unit to generate a filtered sample. The NN filter is applied based on a syntax element of the video unit. The method also includes converting between a video media file and a bitstream based on the filtered sample that was generated.