Image compression distortion can cause performance degradation of machine analysis tasks, therefore recent years have witnessed fast progress in developing deep image compression methods optimized for machine perception. However, the investigation still lacks for saliency segmentation. First, in this paper we propose a deep compression network increasing local signal fidelity of important image pixels for saliency segmentation, which is different from existing methods utilizing the analysis network loss for backward propagation. By this means, these two types of networks can be decoupled to improve the compatibility of proposed compression method for diverse saliency segmentation networks. Second, pixel-level bit weights are modeled with probability distribution in the proposed bit allocation method. The ascending cosine roll-down (ACRD) function allocates bits to those important pixels, which fits the essence that saliency segmentation can be regarded as the pixel-level bi-classification task. Third, the compression network is trained without the help of saliency segmentation, where latent representations are decomposed into base and enhancement channels. Base channels are retained in the whole image, while enhancement channels are utilized only for important pixels, and therefore more bits can benefit saliency segmentation via enhancement channels. Extensive experimental results demonstrate that the proposed method can save an average of 10.34% bitrate compared with the state-of-the-art deep image compression method, where the rate-accuracy (R-A) performances are evaluated on sixteen downstream saliency segmentation networks with five conventional SOD datasets.

Deep learning technologies have been popular in the image compression field for some time. An increasing number of deep-learning-based models are proposed to improve Rate-Distortion (RD) performance. Previous algorithms are implemented in the specific platform and can not be applied in cross-platform environments. In this paper, we present an open-source algorithm library called OpenDIC, which integrates a variety of end-to-end image compression methods in cross-platform environments. The contribution and details of the algorithms used in the library are described. To evaluate the performance of these algorithms, we conduct a comprehensive performance test. We compare and analyze each algorithm according to RD performance, running time, and GPU memory occupancy. The algorithm library has been released at https://openi.pcl.ac.cn/OpenDIC/.