arXiv:2608.28687v1 Announce Type: new
Abstract: Generative models have significantly improved the performance ceiling of image lossy compression at low bitrates by exploiting learned priors. However,...
By Jiarun Chen, Kejun Wu, Li Li, Chengtao Cai, Zhengguo Li, Chia-Wen Lin
The paper introduces MIRC, an overfitted image codec that quantizes all components—including latents, synthesis network, and entropy models—within a single rate‑distortion objective, following the neural video representation codec NVRC. It adds a multi‑scale representation with cross‑stage parameter sharing to capture cross‑scale redundancy, achieving a 10.5% BD‑rate saving over VVC on the CLIC2020 professional validation set. MIRC also offers configurable decoding complexity ranging from 1.2 to 2.9 kMAC per pixel, allowing deployment to match specific resource budgets.
The paper introduces MIRC, an overfitted image codec that quantizes and entropy‑codes all components—including latents, synthesis network, and entropy models—within a single end‑to‑end rate‑distortion framework inspired by NVRC. It adds a multi‑scale representation with cross‑stage parameter sharing to capture cross‑scale redundancy, yielding a 10.5 % BD‑rate saving over VVC on the CLIC2020 professional set. MIRC offers multiple configurations ranging from 1.2 to 2.9 kMAC per pixel, allowing decoding complexity to be tuned to deployment needs.
By Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull
The paper introduces a unified rate–distortion framework for discrete visual tokenization, encompassing vector, product, and scalar quantization. It shows that minimizing distortion, rather than maximizing codebook utilization, is the key objective for reconstruction fidelity and establishes fairness conditions for comparing quantizers. Under these conditions, the study confirms the distortion hierarchy VQ–PQ–SQ and demonstrates that modern VQ methods achieve the lowest distortion.
By Xianghong Fang, Wenlong Mou, Yuan Yuan, Dehan Kong, Tim G. J. Rudner
Scalable Neural Video Representation Compression (S-NVRC) introduces a scalable implicit neural representation (INR) video codec that supports fine-grained bitrate and decoding‑complexity scalability from a single embedded bitstream. It uses a coarse‑to‑fine prefix for feature grids and a nested prefix for network layers, enabling a wide range of operating points while maintaining a single encoding. On the UVG dataset, S‑NVRC outperforms SHM 12.4 and multi‑layer VTM‑20.0 by 43.7 % and 5.6 % in BD‑rate, respectively, and offers flexible complexity scalability.
By Tianhao Peng, Ho Man Kwan, Fan Zhang, Shan Liu, David Bull
arXiv:2608. 04074v1 Announce Type: cross Abstract: Long-context LLM decoding reads the key-value (KV) cache at every step.
By Samuel Fern\'andez-Mendui\~na, Amir Ziashahabi, Eduardo Pavez, Antonio Ortega, Salman Avestimehr