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.

The Collaborative Intelligence (CI) framework offers innovative approaches for deploying Deep Neural Networks (DNNs). However, the limitations of communication resources require minimizing the transmission of bits between the edge and cloud devices to meet the requirements of both machine recognition and human perception. Previous research has demonstrated that the transmission of intermediate layer features of the vision backbone can achieve superior performance in machine vision tasks at very low bit rates without consuming additional bits. Nonetheless, the reduced bit rate poses challenges for image reconstruction. We propose a CI framework that compresses the intermediate features of the Swin Transformer and utilizes a Feature Recovery Module (FRM) to restore crucial information for image reconstruction, thereby satisfying both machine and human visual tasks at low bit rates. Additionally, we introduce a Residual Dual-attention Aggregation Block (RDAB) that exploits both local and global information for effective compression and reconstruction. We conducted experiments on the CUB_200_2011 dataset. The results demonstrate that the proposed method delivers superior performance at low-rate scenarios.