  Deep-learning based techniques have contributed to the remarkable progress in
the field of automatic image quality assessment (IQA). Existing IQA methods are
designed to measure the quality of an image in terms of Mean Opinion Score
(MOS) at the image-level (i.e. the whole image) or at the patch-level (dividing
the image into multiple units and measuring quality of each patch). Some
applications may require assessing the quality at the pixel-level (i.e. MOS
value for each pixel), however, this is not possible in case of existing
techniques as the spatial information is lost owing to their network
structures. This paper proposes an IQA algorithm that can measure the MOS at
the pixel-level, in addition to the image-level MOS. The proposed algorithm
consists of three core parts, namely: i) Local IQA; ii) Region of Interest
(ROI) prediction; iii) High-level feature embedding. The Local IQA part outputs
the MOS at the pixel-level, or pixel-by-pixel MOS - we term it 'pMOS'. The ROI
prediction part outputs weights that characterize the relative importance of
region when calculating the image-level IQA. The high-level feature embedding
part extracts high-level image features which are then embedded into the Local
IQA part. In other words, the proposed algorithm yields three outputs: the pMOS
which represents MOS for each pixel, the weights from the ROI indicating the
relative importance of region, and finally the image-level MOS that is obtained
by the weighted sum of pMOS and ROI values. The image-level MOS thus obtained
by utilizing pMOS and ROI weights shows superior performance compared to the
existing popular IQA techniques. In addition, visualization results indicate
that predicted pMOS and ROI outputs are reasonably aligned with the general
principles of the human visual system (HVS).
