  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.
