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Video Compression With Entropy-Constrained Neural Representations

CVPR 2023 · Video › Video segmentation and tracking · approach: Neural video representations · 21 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace NeRV's fully connected spatial projection with a fully convolutional architecture (AdaIN blocks, single FC for temporal input)NeRV-style frame-based INR · BigBuckBunny representational capacity (12.5M params)PSNR: 41.1 -> 42.7No clear effect
Fully convolutional architecture vs NeRV decoding speedNeRV-style frame-based INR · BigBuckBunny representational capacityFPS: 88 (NeRV) -> 56 (ours) -> 45.3 (E-NeRV)Mixed
Entropy minimization (joint rate-distortion training with quantization-aware training and entropy coding) added to NeRVNeRV · UVGR-D performance (PSNR, MS-SSIM vs bpp)Helped
Joint rate-distortion entropy minimization in place of post-training quantization/pruningproposed INR video compression · UVGR-D performance vs HEVC and DVCHelped
Entropy minimization applied as fine-tuning of a pretrained INR (0.8e epochs with lambda=0, then target lambda)proposed INR video compression · UVGR-D performanceNo clear effect
Per-layer independent entropy modeling of weights vs joint modeling of all weightsproposed INR video compression · UVGR-D performanceHelped
Using a larger architecture (50M) with a smaller (14M) architecture at different bpp ranges, vs one architecture for all ratesproposed INR video compression · UVGR-D performanceHelped

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