Most of the standard image and video codecs are block-based and depending upon the compression ratio the compressed images/videos suffer from different distortions. At low ratios, blurriness is observed and as compression increases blocking artifacts occur. Generally, in order to reduce blockiness, images are low-pass filtered which leads to more blurriness. Also, in bokeh mode images they are commonly seen: blurriness as a result of intentional blurred background while blocking artifact and global blurriness arising due to compression. Therefore, such visual media suffer from both blockiness and blurriness distortions. Along with this, noise is also commonly encountered distortion. Most of the existing works on quality assessment quantify these distortions individually. This paper proposes a methodology to blindly measure overall quality of an image suffering from these distortions, individually as well as jointly. This is achieved by considering the sum of absolute values of low and high-frequency Discrete Frequency Transform (DFT) coefficients defined as sum magnitudes. The number of blocks lying in specific ranges of sum magnitudes including zero-valued AC coefficients and mean of 100 maximum and 100 minimum values of these sum magnitudes are used as feature vectors. These features are then fed to the Machine Learning (ML) based Gaussian Process Regression (GPR) model, which quantifies the image quality. The simulation results show that the proposed method can estimate the quality of images distorted with the blockiness, blurriness, noise and their combinations. It is relatively fast compared to many state-of-art methods, and therefore is suitable for real-time quality monitoring applications.

翻译：大多数标准图像和视频编码器均基于分块操作，根据压缩比不同，压缩后的图像/视频会遭受不同类型的失真。低压缩比时呈现模糊效果，而随着压缩率增加则会出现块效应。通常为减少块效应而采用的低通滤波处理反而会导致更严重的模糊。此外，在背景虚化模式拍摄的图像中常见以下现象：因刻意模糊背景产生的模糊效应，以及压缩导致的块效应与全局模糊。因此这类视觉媒体常同时存在块效应和模糊失真，而噪声也是普遍存在的失真类型。现有质量评估研究大多单独量化这些失真。本文提出一种盲评估方法，可对单独或联合遭受上述失真的图像进行整体质量测量。该方法通过分析离散傅里叶变换（DFT）系数的低频与高频绝对值之和（定义为幅度和）实现。特征向量包括：特定幅度和区间内的块数量（含零值AC系数）、以及这些幅度和中100个最大值与100个最小值的均值。将上述特征输入基于机器学习的高斯过程回归（GPR）模型，即可量化图像质量。仿真结果表明，该方法可有效评估块效应、模糊、噪声及其组合失真图像的质量。相较于众多先进方法，本方法计算速度较快，因此适用于实时质量监控场景。