Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mean Squared Error (MSE) between the reconstructed and original features, while neglecting the importance of inter-feature relationships. In this paper, we analyze the inter-feature relationships, focusing on feature discriminability in machine vision and underscoring its significance in feature compression. To maintain the feature discriminability of reconstructed features, we introduce a discrimination metric for feature compression. The discrimination metric is designed to ensure that the distance between features of the same category is smaller than the distance between features of different categories. Furthermore, we explore the relationship between the discrimination metric and the discriminability of the original features. Experimental results confirm the effectiveness of the proposed discrimination metric and reveal there exists a trade-off between the discrimination metric and the discriminability of the original features.

翻译：特征压缩作为面向机器的视频编码（VCM）的重要分支，已引起广泛关注与探索。然而现有方法主要关注特征内部的相似性，例如重建特征与原始特征之间的均方误差（MSE），却忽视了特征间关系的重要性。本文通过分析特征间关系，重点研究机器视觉中特征的可判别性，并强调其在特征压缩中的关键作用。为保持重建特征的可判别性，我们引入了一种面向特征压缩的判别度量。该判别度量旨在确保同一类别特征之间的距离小于不同类别特征之间的距离。此外，我们进一步探讨了判别度量与原始特征可判别性之间的关系。实验结果验证了所提判别度量的有效性，并揭示了判别度量与原始特征可判别性之间存在权衡关系。