![]() ![]() NR models are widely used for continuous quality monitoring at the receiver end in video playback and streaming systems, with a basic system shown in Figure 1. Since it does not require any information from the source video, it generates less precise scores when evaluating the quality of a video compared to full- and reduced-reference approaches, but can be applied in many real-time applications for which source information is unavailable. No-reference (NR) models can objectively estimate a video’s quality based on the received frames that have been subjected to distortions from coding and transmission losses. An objective 3D video QA is a statistical mathematical model that approximates the results for video perception that would be obtained from typical human viewers. Quality Assessment is an imperative aspect of video services aimed at human observers in applications such as television, Blu-ray, DVD, mobile TV, web TV, gaming, and video streaming. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features. Finally, an objective metric is approximated by extracting these significant perceptual image features. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. ![]() In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. ![]()
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