Decoder-Side Neural Network Fine-Tuning for Adaptive Video Coding

The patent applies different quality levels to different regions of the video content. Important regions (e.g., regions containing objects of interest) are encoded at high quality, while less important regions are encoded at lower quality. This resolves the contradiction by making video quality subjective and region-dependent rather than uniformly high, thereby improving compression efficiency while maintaining perceived quality.

The patent dynamically adjusts quality allocation based on content analysis. The system identifies important regions in each frame and adapts the encoding quality accordingly, rather than using a static quality setting for the entire video. This dynamic approach optimizes the balance between video quality and compression efficiency for each specific region and frame.

The patent performs preliminary offline training to establish a base neural network model with general capabilities. This preliminary action provides training flexibility and establishes a foundation that can be quickly adapted to specific video content through fine-tuning or parameter adjustment during encoding, resolving the contradiction between offline training benefits and content-specific adaptability.

The system enables the neural network to adapt to specific video content through self-service mechanisms during the encoding process. The network automatically adjusts its parameters or selects appropriate pre-trained models based on the characteristics of the input video content, eliminating the need for manual retraining while maintaining adaptability.