One method of evaluating the quality of stereoscopic video is the use of conventional two dimensional (2D) objective metrics. Metrics with good representation of the Human Visual System (HVS) will present more accurate evaluation. In this paper we propose a perceptual based objective metric for 2D videos for 3D video quality evaluation. The proposed Perceptual Quality Metric (PQM) shows better results for 3D video quality evaluation and outperforms the Video Quality Metric (VQM); as it is sensitive to slight changes in image degradation and error quantification starts at pixel level right up to the sequence level. Verifications are done through series of subjective tests to show the level of correlation of PQM and user scores

Detecting defects on surfaces such as steel, can be a challenging task because defects have complex and unique features. These defects happen in many production lines and differ between each one of these production lines. In order to detect these defects, the You Only Look Once (YOLO) detector which uses a Convolutional Neural Network (CNN), is used and received only minor modifications. YOLO is trained and tested on a dataset containing six kinds of defects to achieve accurate detection and classification. The network can also obtain the coordinates of the detected bounding boxes, giving the size and location of the detected defects. Since manual defect detection is expensive, labor-intensive and inefficient, this paper contributes to the sophistication and improvement of manufacturing processes. This system can be installed on chipsets and deployed to a factory line to greatly improve quality control and be part of smart internet of things (IoT) based factories in the future. YOLO achieves a respectable 70.66% mean average precision (mAP) despite the small dataset and minor modifications to the network.

A model exploiting machine learning and content analysis is proposed to predict the subjective quality of stereoscopic videos. This model offers an automated, accurate and consistent subjective quality prediction. The feasibility and accuracy of the proposed technique has been thoroughly analysed with extensive subjective experiments and simulations. Results illustrate that a performance measure of 0.954 in subjective quality prediction can be achieved with the proposed technique

A neural network based technique is proposed to estimate subjective quality of stereoscopic videos. Moreover, to utilize this model for applications where availability of reference signal is not possible to receiver, it applies objective quality of video with minimum dependency on reference signal. This paper presents fast, accurate and consistent subjective quality estimation. Feasibility and accuracy of the proposed technique is thoroughly analyzed with extensive subjective experiments and simulations. Results illustrate that performance measure of 92.3% in subjective quality estimation can be achieved with the proposed technique. © 2014 IEEE

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