The evaluation shows that packet-header features allow an accurate quality prediction when packet losses occur in video transmission.Universidad Nacional Experimental “Francisco de Miranda”Licda. These video quality metrics predict subjective scores that represent well the decoded quality with features extracted from the headers that encapsulate compressed video data. This paper proposes low complexity no-reference video quality metrics for H.264/AVC video transmissions in packet-based networks. These metrics should have high efficiency and low complexity, especially to satisfy the demanding real-time and scalability requirements (many video bitstreams) of the network nodes. In this scenario, packet losses should be detected as early as possible in the transmission chain, preferably inside the network, where perceptual video quality metrics are needed to assess the users Quality of Experience (QoE). In video streaming, bitstreams are often transmitted in best-effort IP networks where impairments such as congestion and varying delay often cause artifacts in the decoded video. Also the contrast maximization algorithm and illumination compensation demonstrated adequate corrections on the infrared images compared to corrections made by the art without adaptability is, when IR radiation is estimated by a pattern, however noise components of the image initial produce some corrections made clearer image noise.
The metrics used correspond to energy, contrast, homogeneity and entropy using the correlation matrix of gray being used in order to characterize and establish IR radiation ranges in which optimal conditions presented images in regard to their characteristics the results obtained show that the eye area used as pattern has features that could be used to estimate changes of IR radiation in the environment, so it can be used to calculate the metrics described above which will serve as input algorithms to maximize contrast and illumination compensation for the purpose of exercising adaptive corrections on image sequences. Radiation in the environment is estimated by using the eye area of the individuals present in the scene, to the region of the face is calculated with different metrics which seeks to establish an estimate of the amount of IR radiation. In this paper we propose a preprocessing method of infrared imaging (IR) system designed to estimate the infrared radiation at the scene of acquisition. Thus, our proposed methodology could pave the way for the development of an automated system to assess road deterioration, which may, in turn, reduce time and costs when designing road infrastructure maintenance plans. The results show a classifier’s accuracy of 96%, a sensitivity of 93.33%, and a Cohen's Kappa coefficient of 93.67%. As classifier, we use a multilayer neural network with a (12123)configuration and trained using the Levenberg–Marquardt algorithm for backpropagation. It is verified using real images of three different pavement distresses: longitudinal cracking, crocodile cracking, and pothole. The proposed methodology consists of six stages: (i) image capture, (ii) image preprocessing, (iii) segmentation via edge detection techniques, (iv) characteristic extraction, (v) classification using neural networks, and (vi) assessment of deteriorated areas. In this paper, we present a methodology to evaluate flexible pavement deterioration using terrestrial photogrammetry techniques and neural networks. However, they can be tedious and subjective and require an experienced evaluator, hence the need to develop new methods for road condition assessment. These two methods serve to determine the severity of damages in flexible and rigid pavements. For this assessment, the Instituto Nacional de Vías(Colombia's National Road Institute) (abbreviated INVIAS in Spanish) uses the Vision Inspection de Zones etItinéraires Á Risque(VIZIR) and Pavement Index Condition (PCI) methods. In Colombia, road deterioration is assessed by means of road inventories and visual inspections.
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