arXiv:2606. 28027v1 Announce Type: cross Abstract: Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility and high computational cost.
arXiv:2606. 03940v1 Announce Type: cross Abstract: In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware.
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.
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.
arXiv:2609.13947v1 Announce Type: cross Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the lo...
arXiv:2607. 14898v1 Announce Type: cross Abstract: Real-time video generation demands fast decoding as much as fast denoising, yet current latent video diffusion models rely on 3D convolutional decoders that are slow and memory-intensive at high resolutions or for long video.
arXiv:2606. 23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost.
MoE-ViE introduces a Mixture-of-Experts vision encoder that scales efficiently for image and video understanding, outperforming dense counterparts across various sizes. The study shows fine‑grained MoE topologies provide significant gains, and proposes an auxiliary‑loss‑free balancing variant and a specialized MoE kernel to reduce inference latency. With frame‑level distillation and a novel freezing mechanism, the largest MoE‑ViE model matches state‑of‑the‑art zero‑shot performance while being 1.7× larger and 76% faster, and it outperforms other encoders when paired with a language model on both image and video benchmarks.
Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost. Although many acceleration methods have been proposed, a central challenge is that the most effective acceleration strategy is highly instance-specific: a recipe that works well for one combination of model, hardware, and inference configuration often does not transfer to another.
arXiv:2606. 04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements.
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.
arXiv:2607. 01921v1 Announce Type: cross Abstract: Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channel coding and incur high latency under limited spectrum availability and fading channels.