Generative image compression produces visually plausible reconstructions at low bitrates, yet their behavior as the rate approaches zero remains largely unexplored. When pushed below normal operating rates, representative codecs undergo semantic collapse: rather than gracefully losing source-specific detail, they produ...
Tian-Yu Zhang, Zhao-Yang Jia, Hou-Qiang Li et al.· 0 citations
This work introduces PULSE, a practical codec that enables low-latency decoding on diverse hardware platforms with an ultra-low-complexity 5.2 kMAC/pixel neural receiver, and efficient bit-exact entropy coding with an integer linear CDF predictor and a meta prior.
Zhao-Yang Jia, Tian-Yu Zhang, Zi-Han Zheng et al.· 0 citations
Mage-VL is presented, an efficient codec-native streaming foundation model for real-time multimodal understanding and interaction and establishes AI4AI data pipelines encompassing prompt-code joint optimization for multimodal captioning and AI-driven performance diagnosis to guide training recipes.
The results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
This work introduces the GenVC, a compression-oriented video diffusion model built on a video diffusion model trained from scratch for compression, and realizes this model directly in pixel space with a global-to-local hierarchy that recovers fine spatio-temporal details, enabling high-quality generative reconstruction...
Naifu Xue, Zhaoyang Jia, Haosen Li et al.· arXiv.org· 0 citations
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