FLLIC：功能无损图像压缩
FLLIC: Functionally Lossless Image Compression
AI总结：
本文提出功能无损图像压缩（FLLIC）范式，对最优去噪后的图像进行无损压缩，以突破数学无损压缩的性能壁垒，在联合去噪与压缩任务中取得最先进性能且计算成本更低。
摘要：
近年来，用于无损图像编码的深度神经网络模型在压缩性能上已超越传统方法，将自然彩色图像此前的无损比特率降低了约百分之十。但即便取得了这些进展，自然图像的数学无损图像压缩（MLLIC）比率目前乃至未来仍难以满足大多数实际成像与视觉系统对带宽和成本效益的要求。为突破MLLIC的性能壁垒，我们对其必要性本身提出质疑。考虑到所有数字成像传感器都受到采集噪声的影响，我们为何还要坚持数学无损编码，即浪费比特来保留噪声？为此，我们提出一种联合去噪与压缩的新范式，称为功能无损图像压缩（FLLIC），它对最优去噪后的图像进行无损压缩（其最优性可依具体任务而定）。尽管相对于含噪输入并非字面意义上的无损，FLLIC旨在实现对潜在无噪声原始图像的最佳可能重建。大量实验表明，FLLIC在含噪图像的联合去噪与压缩方面达到了最先进的性能，并且计算成本更低。
英文摘要：
Recently, DNN models for lossless image coding have surpassed their traditional counterparts in compression performance, reducing the previous lossless bit rate by about ten percent for natural color images. But even with these advances, mathematically lossless image compression (MLLIC) ratios for natural images still fall short of the bandwidth and cost-effectiveness requirements of most practical imaging and vision systems at present and beyond. To overcome the performance barrier of MLLIC, we question the very necessity of MLLIC. Considering that all digital imaging sensors suffer from acquisition noises, why should we insist on mathematically lossless coding, i.e., wasting bits to preserve noises? Instead, we propose a new paradigm of joint denoising and compression called functionally lossless image compression (FLLIC), which performs lossless compression of optimally denoised images (the optimality may be task-specific). Although not literally lossless with respect to the noisy input, FLLIC aims to achieve the best possible reconstruction of the latent noise-free original image. Extensive experiments show that FLLIC achieves state-of-the-art performance in joint denoising and compression of noisy images and does so at a lower computational cost.
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