Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment
This work addresses monotonicity evaluation in quality assessment, which is incremental as it builds on existing methods like SROCC.
The paper tackles the problem of evaluating monotonicity in quality assessment by proposing a differentiable, low-computation global correlation loss and a memory bank mechanism, resulting in performance gains on image and point cloud tasks.
In this paper, we propose a global monotonicity consistency training strategy for quality assessment, which includes a differentiable, low-computation monotonicity evaluation loss function and a global perception training mechanism. Specifically, unlike conventional ranking loss and linear programming approaches that indirectly implement the Spearman rank-order correlation coefficient (SROCC) function, our method directly converts SROCC into a loss function by making the sorting operation within SROCC differentiable and functional. Furthermore, to mitigate the discrepancies between batch optimization during network training and global evaluation of SROCC, we introduce a memory bank mechanism. This mechanism stores gradient-free predicted results from previous batches and uses them in the current batch's training to prevent abrupt gradient changes. We evaluate the performance of the proposed method on both images and point clouds quality assessment tasks, demonstrating performance gains in both cases.