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Neural network stores video and audio as network weights, achieving 2.61x compression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel video codec that stores video and audio as the weights of a neural network, rather than compressed pixel data. This approach uses a sinusoidal representation network (SIREN) that maps space-time coordinates to video and audio values. After training, the network is compressed using knowledge distillation, quantization, and LZMA2 encoding. The resulting compressed representation achieved a significant reduction in file size while maintaining a PSNR of 28.72 dB for video and 24.18 dB for audio, outperforming traditional codecs like H.264 and HEVC in terms of compression ratio. AI

IMPACT This research presents a novel approach to video and audio compression, potentially offering significant file size reductions compared to traditional codecs.

RANK_REASON Academic paper detailing a novel method for video and audio compression using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural network stores video and audio as network weights, achieving 2.61x compression

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Academic paper detailing a novel method for video and audio compression using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nishan Khanal, Saugat Neupane, Abhinav Chalise, Nimesh Gopal Pradhan, Dinesh Baniya Kshatri ·

    Data-driven Video Codec with Implicit Neural Representations

    arXiv:2607.15298v1 Announce Type: cross 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 …

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