Recently, DNN models for lossless image coding have surpassed their
traditional counterparts in compression performance, reducing the 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 break the
bottleneck of MLLIC in compression performance, we question the necessity of
MLLIC, as almost all digital sensors inherently introduce acquisition noises,
making mathematically lossless compression counterproductive. Therefore, in
contrast to MLLIC, 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.

通过联合降噪和压缩的新范式，我们提出了功能性无损图像压缩（Functionally Lossless Image Compression，FLLIC），它对优化降噪图像进行无损压缩（优化可能是任务特定的），虽然不能与有噪输入实现字面上的无损，但旨在实现对潜在无噪原始图像的最佳重建，大量实验证明 FLLIC 在降噪和压缩噪声图像方面具有最先进的性能，并且计算成本更低。

FLLIC：功能无损图像压缩

FLLIC: Functionally Lossless Image Compression

High levels of noise usually exist in today's captured images due to the
relatively small sensors equipped in the smartphone cameras, where the noise
brings extra challenges to lossy image compression algorithms. Without the
capacity to tell the difference between image details and noise, general image
compression methods allocate additional bits to explicitly store the undesired
image noise during compression and restore the unpleasant noisy image during
decompression. Based on the observations, we optimize the image compression
algorithm to be noise-aware as joint denoising and compression to resolve the
bits misallocation problem. The key is to transform the original noisy images
to noise-free bits by eliminating the undesired noise during compression, where
the bits are later decompressed as clean images. Specifically, we propose a
novel two-branch, weight-sharing architecture with plug-in feature denoisers to
allow a simple and effective realization of the goal with little computational
cost. Experimental results show that our method gains a significant improvement
over the existing baseline methods on both the synthetic and real-world
datasets. Our source code is available at
this https URL

针对智能手机拍摄图像噪点问题，提出一种噪点感知的联合去噪与压缩图像算法，通过消除噪点并将其转换为无噪点位，以解决压缩图像时出现的误差位分配问题，实现了一个简单高效的两分支共享权重架构，并在合成和实际的数据集上获得了显著的性能提升。