Performance Evaluation of Natural Scenes Features to create Opinion Unaware-Distortion Unaware IQA Metric

There are many challenges facing image quality assessment (IQA) task. The greatest one which has been treated by this research is the difficulty of quantifying and evaluating distorted images quality blindly with no existence of the original (reference) image or partially from it. Choosing the appropriate features plays a significant role in measuring image quality. This study evaluates the efficiency of a set of features in quantifying image quality. The features have been gathered in spatial domain using the techniques of both rich edges and sharper regions of pristine natural images. The performance efficiency of these features examined through comparing them with both features gathered from reference and distorted images. These techniques employed to build two IQA metrics. Results clearly show the proposed pristine natural features competes reference features in assessing the distorted image quality. This proves the validity of these features in creating a robust metrics for evaluating distorted images. When testing the proposed metrics on LIVE database, experiment results show extracting features by means of rich edges is better than extracting it using sharper regions when assess the prediction monotonicity and applying the prediction accuracy evaluation. Besides they show the average outcome of the two techniques not only competes the popular full-reference peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the developed NR natural image quality evaluator (NIQE) model but also outperform them

Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model

Analysis of Weibull Statistic Features Impact on Image Degradation Measurement

The traditional concept of quality of service (QoS) which focuses on network performance (e.g. packet loss, throughput, and transmission delay), recently has been grown towards the modern concept of quality of experience (QoE). This reflects all user practice including accessing and service provided. In order to maintain the required QoE, it’s necessary for the service provider to recognize and measure image degradation. This study provides different features in order to assess degraded image quality blindly depending on Weibull statistics. Also, it presents a comparison analysis to give the more performing one. The introduced features are originated from the gist of natural scenes (NS) using Weibull distribution of Log-derivatives. These measuring features were collected through both sharper and rich edging regions of the images. Besides, Weibull features were developed by maximum likelihood estimation (MLE) parameters to improve the quality assessment. LIVE database used to calibrate the proposed features achievement. Experiments prove Weibull statistics the best among popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Also, they show Weibull features extracted by means of sharper regions are the best when assess the prediction monotonicity. While applying the prediction accuracy evaluation come up with a good performs when taking the improved Weibull features via sharper regions