Side Information Driven Image Coding for Machines - Zhang, Zhongpeng; Liu, Ying (Picture Coding Symposium, 2022)
With the continuous improvement of computer vision technology, more and more image information is consumed by machines rather than humans. Image coding for machines (ICM) is to compress image data such that they can be more efficiently sent to the receiver side for machines to conduct visual analysis. A typical deep learning-based ICM structure contains one codec network which compresses and transmits images through the Internet and one semantic analysis task network such as image classification and object recognition. In the codec part, the side information is the hyper-prior or hierarchical layers of hyper-priors for the compression of image latent representations. In this paper, we propose a Side Information Driven Image Coding (SIIC) framework based on deep learning. It only compresses and transmits the side information to the receiver for image classification tasks. We obtain a top-l accuracy of 70.38% on the ImageNet1K dataset with 0.046 bits per pixel.