Abstract
In this paper, we present an experimental image quality assessment (IQA) method for image/video patches with compression artifacts. Using the High Efficiency Video Coding (HEVC) standard, we create a new database of image patches with compression artifacts. Then, we conduct a completed subjective testing process to obtain the ‘ground truth’ quality scores for the mentioned database. Finally, we employ an end-to-end learning method to estimate the IQA model for the patches with HEVC compression artifacts. In such proposed method, a modified convolutional neural network (CNN) architecture is exploited for feature extraction while an adaptive moment estimation optimizer solution is used to perform the training process. Experimental results show that the proposed end-to-end IQA method significantly outperforms the relevant IQA benchmarks, especially when the compression artifacts are strongly realized in image/video patches. The proposed IQA method is expected to drive a new set of image/video compression solutions in future image/video coding and transmissions.
Highlights
Image quality assessment (IQA) has been playing a critical part in image and video communications
In a recent work [37], we propose a quality assessment approach database for image patch with the desire to create a new perception-based metric to apply for each region
PROPOSED IMAGE PATCH QUALITY ASSESSMENT Given the necessity of an efficient image quality assessment (IQA) method for image/video patches with compression artifacts, we present a novel end-to-end IQA method
Summary
Image quality assessment (IQA) has been playing a critical part in image and video communications. Subjective assessment methods are highly effective, but they can be infeasible in conducting the assessment in real time and on large scale. It requires the engagement of a number of human viewers who will give their views on image/ video quality under a variety of test conditions. It is necessary that testing conditions be closely monitored, with careful selection of observers and processing of the findings to ensure their consistency and statistical significance.
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