An Edge-based image quality measure (IQM) technique for the assessment of histogram equalization (HE)-based contrast enhancement techniques has been proposed that outperforms the Absolute Mean Brightness Error (AMBE) and Entropy which are the most commonly used IQMs to evaluate Histogram Equalization based techniques, and also the two prominent fidelity-based IQMs which are Multi-Scale Structural Similarity (MSSIM) and Information Fidelity Criterion-based (IFC) measures. The statistical evaluation results show that the Edge-based IQM, which was designed for detecting noise artifacts distortion, has a Person Correlation Coefficient (PCC) > 0.86 while the others have poor or fair correlation to human opinion, considering the Human Visual Perception (HVP). Based on HVP, this paper propose an enhancement to classic Edge-based IQM by taking into account the brightness saturation distortion which is the most prominent distortion in HE-based contrast enhancement techniques. It is tested and found to have significantly well correlation (PCC > 0.87, Spearman rank order correlation coefficient (SROCC) > 0.92, Root Mean Squared Error (RMSE) < 0.1054, and Outlier Ratio (OR) = 0%).
<span>Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.</span>
No reference image quality assessment IQA algorithms are widely used for finding distortions in images without comparing them to references. Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions. Thus, the design of such IQA should take into account the human visual conception. This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels. Statistic objective metrics for correlation coefficient CC which person PCC, spearman rank order SROCC, root mean squared error RMSE are used in the objective evaluation in corresponding with the subjective evaluation. The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.93, RMSE < 0.34. The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use.
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