Tez No	İndirme	Tez Künye	Durumu
598202		Quality estimation and delivery of visual contents with different spatial resolutions / . Yazar:ALİ MURAT DEMİRTAŞ Danışman: PROF. HAMİD JAFARKHANİ Yer Bilgisi: University of California Irvine / Yurtdışı Enstitü Konu:Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol = Computer Engineering and Computer Science and Control ; Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering Dizin:	Onaylandı Doktora İngilizce 2015 93 s.
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In this thesis, we work on quality estimation and delivery of visual content with dierent spatial resolutions. First, we study the quality estimation of images with dierent spatial resolutions. Estimating the quality of the visual content accurately is crucial in providing sat- isfactory multimedia communication. State of the art visual quality assessment approaches are eective when the input image and the reference image have the same resolution. How- ever, nding the quality of an image that has spatial resolution dierent than that of the reference image is still a challenging problem. To solve this problem, we develop a qual- ity estimator (QE) which computes the quality of the input image without resampling the reference or the input images. We begin by identifying the potential weaknesses of previ- ous approaches used to estimate the quality of experience. Next, we design a QE, called Multiscale Image Quality Estimator (MIQE), to estimate the quality of a distorted image with a lower resolution compared to the reference image. We also propose a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. When the input and test images have dierent resolutions, the subjective tests demonstrate that in most cases the proposed method works better than other approaches. In addition, the proposed algorithm also performs well when the reference image and the test image have the same resolution. Second, we examine the quality estimation of videos with dierent spatial resolutions. Full- reference video QEs either resize the distorted input video or the reference video to compute the quality when these videos have dierent spatial resolutions. This resizing operation causes several limitations. MIQE overcomes those limitations for images but it does not consider the temporal characteristics of video. We develop a full-reference video quality estimator that integrates MIQE with the motion information to estimate the quality of the distorted video without resampling the reference or the test videos. We also perform subjective tests to compare the proposed algorithm with the existing QEs. In these tests, the reference and the input videos are displayed at their native resolutions. Test results show that the proposed algorithm outperforms other QEs when the reference video and the input video have dierent spatial resolutions. We have also evaluated the performance of the approach using the Scalable Video Database. Third, we work on the challenge of using a perceptual quality estimator to perform optimum multicasting of videos to the devices with dierent spatial resolutions. We specically focus on the complexity of the optimum perceptual multicasting. The complexity increases due to usage of scalable video coding in combined scalability mode and perceptual quality esti- mators. Using combined scalability increases the number of scalability options, so we need to perform multi-criteria optimization. As a result of the simulations we have performed, we have observed that multi-criteria optimization is not necessary in the low bitrate region, and we propose an algorithm to reduce the complexity of the optimization notably for this region.