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RISS 인기검색어
- 검색키워드
- 저자 : Seshadrinathan, Kalpana (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Video Quality Pooling Adaptive to Perceptual Distortion Severity
Jincheol Park,Seshadrinathan, K.,Sanghoon Lee,Bovik, A. C. IEEE 2013 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.22 No.2
<P>It is generally recognized that severe video distortions that are transient in space and/or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database.</P>
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No-Reference Sharpness Assessment of Camera-Shaken Images by Analysis of Spectral Structure
Taegeun Oh,Jincheol Park,Seshadrinathan, Kalpana,Sanghoon Lee,Bovik, Alan Conrad IEEE 2014 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.23 No.12
<P>The tremendous explosion of image-, video-, and audio-enabled mobile devices, such as tablets and smart-phones in recent years, has led to an associated dramatic increase in the volume of captured and distributed multimedia content. In particular, the number of digital photographs being captured annually is approaching 100 billion in just the U.S. These pictures are increasingly being acquired by inexperienced, casual users under highly diverse conditions leading to a plethora of distortions, including blur induced by camera shake. In order to be able to automatically detect, correct, or cull images impaired by shake-induced blur, it is necessary to develop distortion models specific to and suitable for assessing the sharpness of camera-shaken images. Toward this goal, we have developed a no-reference framework for automatically predicting the perceptual quality of camera-shaken images based on their spectral statistics. Two kinds of features are defined that capture blur induced by camera shake. One is a directional feature, which measures the variation of the image spectrum across orientations. The second feature captures the shape, area, and orientation of the spectral contours of camera shaken images. We demonstrate the performance of an algorithm derived from these features on new and existing databases of images distorted by camera shake.</P>
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