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Recent advancements in image compression techniques have focused on improving efficiency and fidelity across various applications. Structured Gaussian Image (SGI) and Graph-based Learned Image Compression (GLIC) leverage innovative frameworks to enhance compression ratios while maintaining image quality. SGI utilizes a seed-based approach to optimize high-resolution images, achieving significant compression and faster optimization. GLIC employs Graph Neural Networks to adaptively model redundancy, resulting in superior performance compared to traditional methods. Additionally, frameworks like ASSR-EIC and DiffCR explore joint super-resolution and diffusion-based methods to enhance low-bitrate image reconstruction. These developments are crucial for builders aiming to implement efficient image processing solutions in resource-constrained environments, ensuring high-quality visual outputs without excessive storage demands.
2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices. However, scaling to high-resolution images requires optimizing and ...
Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inher...
Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each ta...
Raw images preserve linear sensor measurements and high bit-depth information crucial for advanced vision tasks and photography applications, yet their storage remains challenging due to large file si...
Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution d...
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despit...
Recent advancements in diffusion-based generative priors have enabled visually plausible image compression at extremely low bit rates. However, existing approaches suffer from slow sampling processes ...
Recently, progress has been made on the Intra Pattern Copy (IPC) tool for JPEG XS, an image compression standard designed for low-latency and low-complexity coding. IPC performs wavelet-domain intra c...
We present a novel paradigm for ultra-low-bitrate image compression (ULB-IC) that exploits the ``temporal'' evolution in generative image compression. Specifically, we define an explicit intermediate ...
Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-bas...
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Canonical ID image-compression | Route /topic/image-compression
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}Use This Via API or MCP
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