Feature Quality Assessment: A Database and A Lightweight Objective Method

In the era of Artificial Intelligence, visual data gathered by edge devices could be primarily utilized for machine vision tasks. The prominent coding frameworks accomplish this by extracting and compressing features extracted from input data. As such, the quality of these features is vital, as they reflect the performance of the coding framework. However, much less work has been dedicated to quality assessment on features, impeding the optimization of the coding system. In this work, we pioneer to explore the feature quality assessment by creating a novel database tailored for features, with the quality ground-truth for each feature. Then, we propose a lightweight feature quality assessment method, called Lightweight Feature Quality Assessment (LFQA). We analyze the feature characteristics from the perspective of spatial and channel thoroughly, and the framework of LFQA is designed based on the analysis results. Experimental results demonstrate that LFQA accurately evaluates the quality of features, reaching a notable Spearman Rank-Order Correlation Coefficient of 85.38%, and exhibits competitive performance in improving the performance of video coding for machine system. Furthermore, LFQA has fewer model parameters and faster inference speed, ensuring a wide range of promising applications.