Neural Video Representation for Redundancy Reduction and Consistency Preservation

Implicit neural representations (INRs) embed various signals into networks. They have gained attention in recent years because of their versatility in handling diverse signal types. For videos, INRs achieve video compression by embedding video signals into networks and compressing them. Conventional methods use an index that expresses the time of the frame or the features extracted from the frame as inputs to the network. The latter method provides greater expressive capability as the input is specific to each video. However, the features extracted from frames often contain redundancy, which contradicts the purpose of video compression. Moreover, since frame time information is not explicitly provided to the network, learning the relationships between frames is challenging. To address these issues, we aim to reduce feature redundancy by extracting features based on the high-frequency components of the frames. In addition, we use feature differences between adjacent frames in order for the network to learn frame relationships smoothly. We propose a video representation method that uses the high-frequency components of frames and the differences in features between adjacent frames. The experimental results show that our method outperforms the existing HNeRV method in 90 percent of the videos.