Implicit Neural Representations (INRs) have recently demonstrated impressive performance for video compression. However, since a separate INR must be overfit for each video, scaling to high-resolution videos while maintaining encoding efficiency remains a significant challenge. Hypernetwork-based approaches predict INR weights (hyponetworks) for unseen videos at high speeds, but with low quality, large compressed size, and prohibitive memory needs at higher resolutions. We address these fundamental limitations through three key contributions: (1) an approach that decomposes the weight prediction task spatially and temporally, by breaking short video segments into patch tubelets, to reduce pretraining memory overhead across resolutions; (2) a residual-based storage scheme that captures only differences between consecutive segment representations, significantly reducing bitstream size; and (3) a temporal coherence regularization framework that encourages changes in the weight space to be correlated with changes in video, making residuals even smaller. Our proposed method, TeCoNeRV, achieves substantial improvements of 2.47dB and 4.94dB PSNR over the baseline at 480p and 720p on UVG, with 36% lower bitrates and 1.5-3${\times}$ faster encoding speeds. With our low memory usage, we are the first hypernetwork approach to demonstrate results at $480$p, $720$p and $1080$p on UVG, HEVC and MCL-JCV. Our project page is available at https://namithap10.github.io/teconerv/.

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