HTTP adaptive streaming is a widely used method for delivering the video content to its final recipients. The visual quality of the streamed video content is being adaptively changed according to network conditions to offer the user a smooth playback, which is even more important for mobile connection like LTE. In this paper, we focus on the encoding of the video content and on the segmentation of the video to be used in DASH based service. We used long sequences with duration up to 2.5 hours to simulate a real life situation. We investigate the influence of the GOP length on the final DASH segment size and evaluate the performance of AVC and HEVC when used in DASH. We used several fixed values of GOP length and one special case of scene change based GOP creation. Our results showed, that such an adaptive segmentation mode brings up to 11% bitrate savings with preserving comparable quality and lower fluctuations in absolute size of the DASH segments.
Efficient video compression algorithms in advanced multimedia broadcasting systems are in high demand. In the last decades, different video compression tools have been developed which can influence the final Quality of Experience in different ways. his paper has two goals. The first goal is to present a study of different compression algorithms available for stereoscopic 3D videos. The second goal is to present the possibilities in the creation of new stereoscopic models. The well-established video codecs (AVC, MVC, HEVC and MV-HEVC) are considered as encoders. Generic objective video quality metrics are used to analyze the compression efficiencies of the considered codecs, extended with results from subjective tests. The correlations between the objective and subjective scores are analyzed statistically. Due to unsatisfactory results of generic 2D metrics for the stereoscopic sequences used in the test, new objective models are presented. Such models show improved correlation with subjective stereoscopic video quality. The validation, verification and a description of models are presented in detail.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.