论文标题
感知启发的加权MSE优化使用不规则感知图傅立叶变换
Perceptually inspired weighted MSE optimization using irregularity-aware graph Fourier transform
论文作者
论文摘要
在图像和视频编码应用中，传统上使用均方误差（MSE）测量了失真，这表明使用正交变换，例如离散的余弦变换（DCT）。通常在编码后使用感知指标，例如结构相似性（SSIM），但不与编码过程相关。在本文中，我们考虑了一个替代框架，其目标是优化加权MSE指标，其中可以将不同的权重分配给每个像素，以反映其在感知图像质量方面的相对重要性。为此，我们提出了一种基于不规则的图形傅立叶变换（IAGFT）的新型变换编码方案，其中诱导的iagft是正交的，但是正交性是根据与加权MSE相对应的内部产物定义的。我们建议使用从输入图像的局部方差得出的权重，以使加权MSE与SSIM对齐。这样，相关的IAGFT可以基于DCT来实现SSIM的编码效率提高。我们的实验结果表明，在测试图像上的多尺度SSIM方面有压缩增益。
In image and video coding applications, distortion has been traditionally measured using mean square error (MSE), which suggests the use of orthogonal transforms, such as the discrete cosine transform (DCT). Perceptual metrics such as Structural Similarity (SSIM) are typically used after encoding, but not tied to the encoding process. In this paper, we consider an alternative framework where the goal is to optimize a weighted MSE metric, where different weights can be assigned to each pixel so as to reflect their relative importance in terms of perceptual image quality. For this purpose, we propose a novel transform coding scheme based on irregularity-aware graph Fourier transform (IAGFT), where the induced IAGFT is orthogonal, but the orthogonality is defined with respect to an inner product corresponding to the weighted MSE. We propose to use weights derived from local variances of the input image, such that the weighted MSE aligns with SSIM. In this way, the associated IAGFT can achieve a coding efficiency improvement in SSIM with respect to conventional transform coding based on DCT. Our experimental results show a compression gain in terms of multi-scale SSIM on test images.
