Title: Rethinking Video-INRs through Perceptual Optimization
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Implicit neural representation, Video Compression, Video Representation, Perceptual Optimization

Abstract: Implicit neural representations (INRs) have recently emerged as a powerful paradigm for video modeling, representing videos as continuous functions parameterized by network weights, rather than storing raw pixels or latent codes. Despite architectural progress, most video-INR methods largely persist with pixel-wise (MSE or $\ell_1$) losses.
Through the lens of variational inference, we show—both theoretically and empirically—that these pixel-wise objectives implicitly assume Gaussian or Laplacian error distributions, which are statistically misaligned with per-video characteristics, where errors are highly structured and temporally correlated. To address this limitation, we propose shifting supervision from the pixel domain to perceptual feature spaces, which provide stable transformation spaces that relax restrictive distributional assumptions and align optimization with perceptual semantics. Specifically, we introduce two feature-domain objectives: Multi-Vision Feature Similarity (MVFS) for intra-frame fidelity and Vision Subject Similarity (VSS) for inter-frame temporal consistency. Even with a lightweight INR backbone using simple cascaded upsampling, our method surpasses state-of-the-art VAE- and diffusion-based codecs in perceptual quality while maintaining real-time decoding at an average of $\sim$125 FPS on 1080p resolution. Our results demonstrate that perceptual supervision provides a principled and promising direction for advancing video-INRs.