CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation

• URL: http://arxiv.org/abs/2501.00975v1
• Date: Wed, 01 Jan 2025 22:58:06 GMT
• Title: CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation
• Authors: Daniel Silver, Ron Kimmel,
• Abstract summary: Implicit Neural Representation (INR) is a promising alternative to traditional transform-based methodologies. We introduce CoordFlow, a novel pixel-wise INR for video compression. It yields state-of-the-art results compared to other pixel-wise INRs and on-par performance compared to leading frame-wise techniques.
• Score: 11.364753833652182
• License:
• Abstract: In the field of video compression, the pursuit for better quality at lower bit rates remains a long-lasting goal. Recent developments have demonstrated the potential of Implicit Neural Representation (INR) as a promising alternative to traditional transform-based methodologies. Video INRs can be roughly divided into frame-wise and pixel-wise methods according to the structure the network outputs. While the pixel-based methods are better for upsampling and parallelization, frame-wise methods demonstrated better performance. We introduce CoordFlow, a novel pixel-wise INR for video compression. It yields state-of-the-art results compared to other pixel-wise INRs and on-par performance compared to leading frame-wise techniques. The method is based on the separation of the visual information into visually consistent layers, each represented by a dedicated network that compensates for the layer's motion. When integrated, a byproduct is an unsupervised segmentation of video sequence. Objects motion trajectories are implicitly utilized to compensate for visual-temporal redundancies. Additionally, the proposed method provides inherent video upsampling, stabilization, inpainting, and denoising capabilities.

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