The paper introduces the first bound‑propagation framework for verifying the decoder of Deep Joint Source‑Channel Coding (DeepJSCC), providing worst‑case reconstruction error guarantees over a specified wireless channel noise region. It extends existing DNN verification techniques to handle PReLU activations, transposed convolutions, and Rayleigh fading, and incorporates Lipschitz‑regularized global robustness training (GloRo) to tighten certification bounds. Experiments on image transmission models show significant improvements in certified safety and real‑world OFDM validation confirms the theoretical guarantees.
By Thanh Le, Hai Duong, Takeshi Matsumura, ThanhVu Nguyen
LRConv-NeRV introduces low‑rank separable convolutions into the NeRV neural video decoder, replacing selected dense 3x3 layers to reduce computational load and memory usage. By applying low‑rank factorization progressively from the largest to earlier decoder stages, the method offers controllable trade‑offs between reconstruction quality and efficiency. Experiments show that applying LRConv only to the final decoder stage cuts decoder complexity by 68% and model size by 9.3% with negligible quality loss, while INT8 quantization preserves performance close to the dense baseline.
By Tamer Shanableh
arXiv:2609.31620v1 Announce Type: new
Abstract: Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual r...
By Hongyang Du, Yunfei Xie, Junjie Ye, Jiawei Yang, Xiaoyan Cong, Haodong Zhang, Yongchao Huang, Haiyu Wu, Zongxia Li, Shihang Gui, Dawei Liu, Runhao Li, Jingcheng Ni, Chen Wei, Randall Balestriero, Yue Wang
The paper proposes a new strategy for merging layers in deep neural networks, enabling depth compression without requiring an analytical solution for convolutions with padding and without increasing kernel size. This approach addresses limitations of previous methods that struggled with padded convolutions and larger kernels, and it is validated across various architectures and datasets with measured inference speed-ups on embedded platforms.
By Petro Shulzhenko, Gabriele Spadaro, Enzo Tartaglione
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
arXiv:2609.39296v1 Announce Type: new
Abstract: Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most exist...
By Xiangben Zhu, Caili Guo, Yang Yang, Chuanhong Liu, Meiyi Zhu