  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.
