SMR: Satisfied Machine Ratio Modeling for Machine Recognition-Oriented Image and Video Compression

Tons of images and videos are fed into machines for visual recognition all the time. Like human vision system (HVS), machine vision system (MVS) is sensitive to image quality, as quality degradation leads to information loss and recognition failure. In recent years, MVS-targeted image processing, particularly image and video compression, has emerged. However, existing methods only target an individual machine rather than the general machine community, thus cannot satisfy every type of machine. Moreover, the MVS characteristics are not well leveraged, which limits compression efficiency. In this paper, we introduce a new concept, Satisfied Machine Ratio (SMR), to address these issues. SMR statistically measures the image quality from the machine's perspective by collecting and combining satisfaction scores from a large quantity and variety of machine subjects, where such scores are obtained with MVS characteristics considered properly. We create the first large-scale SMR dataset that contains over 22 million annotated images for SMR studies. Furthermore, a deep learning-based model is proposed to predict the SMR for any given compressed image or video frame. Extensive experiments show that using the SMR model can significantly improve the performance of machine recognition-oriented image and video compression. And the SMR model generalizes well to unseen machines, compression frameworks, and datasets.