论文标题
部分可观测时空混沌系统的无模型预测
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
论文作者
论文摘要
由于其对多媒体存储和传输的重要性,图像压缩最近引起了广泛的兴趣。同时,对压缩图像的可靠图像质量评估(IQA)不仅可以帮助验证各种压缩算法的性能,而且还有助于指导压缩优化。在本文中,我们设计了一个全参考图像质量评估度量标准swiniqa,以测量在学到的SWIN距离空间中压缩图像的感知质量。众所周知,压缩伪像通常具有不同的失真类型和程度的分布。为了将压缩图像扭曲到共享表示空间中,同时维护复杂的失真信息,我们从Swin Transformer的每个阶段提取层次特征表示。此外,我们利用交叉注意操作将提取的特征表示形式映射到学习的Swin距离空间中。实验结果表明,与传统方法和基于学习的方法相比,所提出的指标与人类感知判断的一致性更高。
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed with diverse distortion types and degrees. To warp the compressed images into the shared representation space while maintaining the complex distortion information, we extract the hierarchical feature representations from each stage of the Swin Transformer. Besides, we utilize cross attention operation to map the extracted feature representations into a learned Swin distance space. Experimental results show that the proposed metric achieves higher consistency with human's perceptual judgment compared with both traditional methods and learning-based methods on CLIC datasets.