Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment

• URL: http://arxiv.org/abs/2501.15485v1
• Date: Sun, 26 Jan 2025 11:09:16 GMT
• Title: Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment
• Authors: Yipeng Liu, Qi Yang, Yiling Xu,
• Abstract summary: We propose a differentiable, low-computation monotonicity evaluation loss function and a global perception training mechanism. We evaluate the performance of the proposed method on both images and point clouds quality assessment tasks.
• Score: 20.09599895154013
• License:
• Abstract: 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.

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