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arXiv · 2607.15298

Data-driven Video Codec with Implicit Neural Representations

Nishan Khanal·Saugat Neupane·Abhinav Chalise·Nimesh Gopal Pradhan·Dinesh Baniya Kshatri
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Abstract

A conventional codec stores a video as compressed pixel data. We instead store the video, together with its audio track, as the weights of a single sinusoidal representation network (SIREN) that maps space-time coordinates to RGB values and audio amplitudes. The network uses separate audio and video initialization layers, a stack of shared fully connected hidden layers, and three output branches: one for video and two Siamese audio branches whose disagreement is used to estimate and subtract residual noise. The overfitted teacher network is then compressed by response-based knowledge distillation into a smaller student, followed by 16-bit symmetric weight quantization and lossless LZMA2 (xz) encoding. On a 6.08 MiB test video, the quantized student reaches a video PSNR of 28.72 dB with SSIM of 0.75, and an audio PSNR of 24.18 dB with a log spectral distance of 10.69 dB, while the pipeline shrinks the representation from 9.05 MiB to 2.33 MiB, an overall compression ratio of 2.61. A bit-width sweep from 1-bit to 32-bit quantization shows that reconstruction quality saturates at 16 bits. We compare against H.264, HEVC, and MP3, report where the approach falls short of them, and describe a browser-based prototype that trains, transfers, and decodes these models over WebRTC.

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Nishan Khanal, Saugat Neupane, Abhinav Chalise, Nimesh Gopal Pradhan, Dinesh Baniya Kshatri. 2026-07-02. Data-driven Video Codec with Implicit Neural Representations. https://arxiv.org/abs/2607.15298

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