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Interactive Subjective Study on Picture-level Just Noticeable Difference of Compressed Stereoscopic Images

机译:压缩立体图像在图像水平上的显着差异的交互式主观研究

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The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picture-level JND (PJND) of compressed stereo images. The study, which involves 48 subjects and 10 stereoscopic images compressed with H.265 intra coding and JPEG2000, includes two parts. In the first part, we determine the minimum distortion that the HVS can perceive against a pristine stereo image. In the second part, we explore the minimum distortion that each subject perceives against a distorted stereo image. Modeling the distribution of the PJND samples as Gaussian, we obtain their complementary cumulative distribution functions, which are known as Satisfied User Ratio (SUR) functions. Statistical analysis results demonstrate that the SUR is highly dependent on the image contents. The HVS is more sensitive to distortion in images with more texture details. The compressed stereoscopic images and the PJND samples are collected in a data set called SIAT-JSSI, which we release to the public.
机译:显着差异(JND)揭示了人类视觉系统(HVS)可以感知的最小失真。传统的JND研究主要集中在背景亮度适应和对比度掩盖上。但是,HVS不会基于单个像素或块,而是基于整个图像来感知视觉内容。在这项工作中,我们对压缩的立体图像的图片级JND(PJND)进行了交互式主观视觉质量研究。这项研究包括48个主题,并用H.265帧内编码和JPEG2000压缩了10个立体图像,包括两个部分。在第一部分中,我们确定了HVS可以针对原始立体图像感知到的最小失真。在第二部分中,我们探讨了每个主体针对扭曲的立体声图像所感知的最小失真。将PJND样本的分布建模为高斯模型,我们获得了它们的互补累积分布函数,这就是“满意用户比率”(SUR)函数。统计分析结果表明,SUR高度依赖于图像内容。 HVS对具有更多纹理细节的图像失真更敏感。压缩的立体图像和PJND样本收集在一个名为SIAT-JSSI的数据集中,我们向公众发布。

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