  Video Coding for Machines (VCM) aims to compress visual signals for machine
analysis. However, existing methods only consider a few machines, neglecting
the majority. Moreover, the machine's perceptual characteristics are not
leveraged effectively, resulting in suboptimal compression efficiency. To
overcome these limitations, this paper introduces Satisfied Machine Ratio
(SMR), a metric that statistically evaluates the perceptual quality of
compressed images and videos for machines by aggregating satisfaction scores
from them. Each score is derived from machine perceptual differences between
original and compressed images. Targeting image classification and object
detection tasks, we build two representative machine libraries for SMR
annotation and create a large-scale SMR dataset to facilitate SMR studies. We
then propose an SMR prediction model based on the correlation between deep
feature differences and SMR. Furthermore, we introduce an auxiliary task to
increase the prediction accuracy by predicting the SMR difference between two
images in different quality. Extensive experiments demonstrate that SMR models
significantly improve compression performance for machines and exhibit robust
generalizability on unseen machines, codecs, datasets, and frame types. SMR
enables perceptual coding for machines and propels VCM from specificity to
generality. Code is available at https://github.com/ywwynm/SMR.
