Learning-based Compression for Noisy Images in the Wild

Abstract

Digital images in real world applications typically undergo a wide variety of quality degradations before compression or re-compression. Existing learning based codecs are typically data-driven, relying on the predefined compression pipeline with pristine or high quality images as the input. However, the images in the wild may exhibit the substantially different characteristics compared to the high quality images, casting major challenges to the learning based image coding. In this paper, we propose a robust noisy image compression framework with the blind assumption on the specific noise type and level. The specifically designed encoder decomposes the representation of visual content into two types of features, including the Features that represent the Intrinsic Content (FIC) and the Features that account for Additive Degradation (FAD). As such, beyond the philosophy of faithfully reconstructing the given image with high fidelity, only FIC needs to be compactly represented and conveyed. The principled disentanglement strategy facilitates the removal of the redundancy from multiple perspectives (e.g., spatial, channel and content), ensuring the handling of a wide variety of noisy images in the wild. Extensive experimental results show that our model can achieve superior performance in terms of the ultimate quality and exhibit the strong generalizability across images degraded by a variety of means. The proposed scheme also points out a new research avenue on learning based compression for images in the wild, which is technically challenging but desirable in practice.