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.

翻译：面向机器的视频编码（VCM）旨在压缩视觉信号以供机器分析。然而，现有方法仅考虑少数机器，忽略了大多数机器。此外，机器的感知特性未被有效利用，导致压缩效率欠佳。为克服这些局限，本文引入满意机器比（SMR）——一种通过聚合机器对压缩图像/视频的满意度评分来统计评估其感知质量的指标。每个评分源于原始图像与压缩图像之间的机器感知差异。针对图像分类与目标检测任务，我们构建了两个代表性机器库用于SMR标注，并创建大规模SMR数据集以促进相关研究。随后，基于深度特征差异与SMR之间的相关性，提出SMR预测模型。此外，我们引入辅助任务，通过预测不同质量图像间的SMR差异来提升预测精度。大量实验表明，SMR模型显著提升了面向机器的压缩性能，并在未见机器、编解码器、数据集及帧类型上展现出强泛化能力。SMR实现了面向机器的感知编码，推动VCM从特例性走向通用性。代码见https://github.com/ywwynm/SMR。