Neural Network Generalized Difference Coder for Video Compression
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Solution Overview
Problem
Current video coding technologies face challenges in achieving efficient compression and decompression while maintaining high picture quality, particularly in limited bandwidth scenarios, as they struggle to effectively exploit redundancies in video data.
Innovation Solution
The use of neural networks, specifically convolutional neural networks, for encoding and decoding video data by processing generalized residuals and prediction signals, which allows for improved redundancy detection and bitstream reduction through operations like generalized difference and sum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard video coding techniques are used, then compression is achieved, but picture quality deteriorates in limited bandwidth scenarios
Solution Approach 1:
The patent replaces traditional mechanical video coding operations (motion estimation, transform coding) with neural network-based operations. The encoder uses neural networks to perform residual coding and the decoder uses neural networks to reconstruct video frames, substituting conventional signal processing mechanisms with learning-based systems that can adaptively optimize compression while preserving quality.
Solution Approach 2:
The patent changes the fundamental parameters of video coding by introducing learned transformations instead of fixed mathematical transforms. The neural networks learn optimal parameter representations for residuals and predictions, allowing dynamic adaptation of coding parameters based on content characteristics, thereby improving the rate-distortion performance.
2Productivity
If compression ratio is increased, then bandwidth efficiency improves, but picture quality is lost
Solution Approach 1:
The patent implements feedback mechanisms where the encoder neural network receives information about the reconstructed signal quality and adjusts its coding strategy accordingly. The system uses learned models to predict distortion and provides feedback to optimize the compression process, allowing dynamic adjustment of compression levels to maintain quality while improving ratio.
Solution Approach 2:
The patent introduces dynamic adaptability into the coding system through neural networks that can adjust their behavior based on input content characteristics. Rather than using fixed compression algorithms, the system dynamically optimizes coding parameters and transformations according to the specific video content, enabling variable compression ratios while maintaining quality thresholds.
3Device complexity
If traditional residual coding is used, then encoding simplicity is maintained, but redundancy detection is insufficient
Solution Approach 1:
The patent segments the video coding process into distinct neural network modules: one for residual coding and another for prediction. This segmentation allows each module to specialize in detecting different types of redundancies while maintaining overall system manageability. The modular neural network architecture processes different aspects of redundancy detection separately, improving overall detection capability without excessive complexity.
Data Source
AI summary
This application provides methods and apparatuses for encoding image or video related data into a bitstream. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. A neural network (generalized difference) is applied to a signal and a predicted signal during the encoding to obtain a generalized residual. During the decoding another neural network (generalized sum) may be applied to a reconstructed generalized residual and the predicted signal to obtain a reconstructed signal.