Despite the rapid progress of restoration-aware compression, existing studies still face challenges in low-bitrate restoration scenarios. Real-world image acquisition is inevitably affected by degradations, such as noise, blur, and adverse weather, while subsequent compression further reduces perceptual quality. These coupled degradations make it difficult to recover high-quality images from compact bitstreams. To address this issue, we propose a Decoupled Energy-Complementary method for low-bitrate perceptual restoration-aware compression (DEC). DEC jointly considers acquisition distortions and compression effects within a unified restoration framework. It adopts a decoupled dual-branch architecture with a MainInfo Branch and a SideInfo Branch. The MainInfo Branch preserves structural information for reliable reconstruction, whereas the SideInfo Branch compactly encodes boundary and texture cues with only a few additional bits. These two branches form a compact but informative representation that balances compression efficiency and restoration quality. Extensive experiments show that the SideInfo Branch enhances local restoration details across bitrates, while the MainInfo Branch maintains structural fidelity. Overall, DEC achieves an average 35.49% BD-rate reduction with consistent LPIPS gains over state-of-the-art restoration-aware compression methods, confirming its efficiency and visual quality.
Yuan Xue, Qi Zhang, Shiqi Wang et al.· IEEE Transactions on Image P...· 0 citations
This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic coupling of tool modifications. In particular, we present an empirical case study on the Planar mode, a long-standing intra prediction tool in video coding standards. Our experiments operate within a generation-and-evaluation loop, with the LLM generating new Planar predictors, encoder trials evaluating their coding performance, and the LLM re-generating refined implementations based on the evaluation feedback. We first examine directly replacing the default Planar mode in the Fraunhofer Versatile Video Encoder (VVenC) under its faster preset. Experimental results demonstrate that the LLM-generated mode can outperform the conventional Planar mode on this lightweight toolset, achieving 0.18% bitrate savings with 0.4% complexity overhead on the standard benchmark. We further extend our evaluation to the Enhanced Compression Model (ECM). Leveraging newly introduced directional Planar modes, we investigate two integration strategies: directly replacing them, and introducing the LLM-generated predictor as an additional prediction mode with new syntax elements. The empirical results suggest that both strategies can yield coding gains under a constrained low-resolution setting. Overall, this study offers preliminary evidence and practical insights, highlighting both the potential and open challenges of LLM-based coding tool design.
Yingwen Zhang, Meng Wang, Li-Qiang He et al.· 0 citations
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