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Learning Raw Image Reconstruction-Aware Deep Image Compressors
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2019-03-04 , DOI: 10.1109/tpami.2019.2903062
Abhijith Punnappurath , Michael S. Brown

Deep learning-based image compressors are actively being explored in an effort to supersede conventional image compression algorithms, such as JPEG. Conventional and deep learning-based compression algorithms focus on minimizing image fidelity errors in the nonlinear standard RGB (sRGB) color space. However, for many computer vision tasks, the sensor's linear raw-RGB image is desirable. Recent work has shown that the original raw-RGB image can be reconstructed using only small amounts of metadata embedded inside the JPEG image [1] . However, [1] relied on the conventional JPEG encoding that is unaware of the raw-RGB reconstruction task. In this paper, we examine the ability of deep image compressors to be “aware” of the additional objective of raw reconstruction. Towards this goal, we describe a general framework that enables deep networks targeting image compression to jointly consider both image fidelity errors and raw reconstruction errors. We describe this approach in two scenarios: (1) the network is trained from scratch using our proposed joint loss, and (2) a network originally trained only for sRGB fidelity loss is later fine-tuned to incorporate our raw reconstruction loss. When compared to sRGB fidelity-only compression, our combined loss leads to appreciable improvements in PSNR of the raw reconstruction with only minor impact on sRGB fidelity as measured by MS-SSIM.

中文翻译:

学习可识别原始图像的深层图像压缩器

为了取代传统的图像压缩算法(例如JPEG),正在积极探索基于深度学习的图像压缩器。基于深度学习的传统压缩算法着眼于最小化非线性标准RGB(sRGB)颜色空间中的图像保真度误差。但是,对于许多计算机视觉任务,需要传感器的线性原始RGB图像。最近的工作表明,仅使用嵌入在JPEG图像内的少量元数据就可以重建原始的RGB原始图像。[1] 。然而,[1]依赖于不知道原始RGB重建任务的常规JPEG编码。在本文中,我们研究了深度图像压缩器“意识到”原始重建的附加目标的能力。为了实现这一目标,我们描述了一个通用框架,该框架使针对图像压缩的深度网络能够共同考虑两个都图像保真度误差和原始重建误差。我们在两种情况下描述这种方法:(1)使用我们提出的联合损失从零开始训练网络,以及(2)最初仅针对sRGB保真度损失训练的网络随后进行微调以合并我们的原始重建损失。与仅sRGB保真度压缩相比,我们的综合损失导致原始重建的PSNR有了明显改善,而对MS-SSIM测得的sRGB保真度影响很小。
更新日期:2020-03-06
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