Image processing on compressed data for large video databases

Arman, Farshid; Hsu, Arding; Chiu, Ming-Yee

doi:10.1145/166266.166297

Cited by 165 publications

(70 citation statements)

References 15 publications

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“…Automated extraction of key frames has been addressed by many researchers [1], [2], [4], [6]- [9], [13], [16], [17], [19]- [26]. A first attempt to automate the key-frame extraction was to choose as a key frame the frame appearing after each detected shot boundary [16].…”

Section: A Key-frame Extractionmentioning

confidence: 99%

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

Hanjalic

Zhang

1999

IEEE Trans. Circuits Syst. Video Technol.

286

115

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Abstract-Key frames and previews are two forms of a video abstract, widely used for various applications in video browsing and retrieval systems. We propose in this paper a novel method for generating these two abstract forms for an arbitrary video sequence. The underlying principle of the proposed method is the removal of the visual-content redundancy among video frames. This is done by first applying multiple partitional clustering to all frames of a video sequence and then selecting the most suitable clustering option(s) using an unsupervised procedure for clustervalidity analysis. In the last step, key frames are selected as centroids of obtained optimal clusters. Video shots, to which key frames belong, are concatenated to form the preview sequence.Index Terms-Clustering, cluster-validity analysis, contentbased video retrieval, content classification, video content analysis.

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Section: A Key-frame Extractionmentioning

confidence: 99%

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

Hanjalic

Zhang

1999

IEEE Trans. Circuits Syst. Video Technol.

286

115

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“…We begin with some function f for comparing two images on a pixel-by-pixel basis. We search for the integer values of xand ythat maximize the quantity X x y f(I x + x y+ y ] I 0 x y]) (2) where the sum is taken over all pixels. We maximize over some range of possible motions k x k + k y k : (3) Note that we only search for a translational motion b etween the two images.…”

Section: Motion Compensationmentioning

confidence: 99%

A feature-based algorithm for detecting and classifying production effects

1999

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We describe a new approach to the detection and classication of scene breaks in video sequences. Our method can detect and classify a variety of scene breaks, including cuts, fades, dissolves and wipes, even in sequences involving signi cant motion. We detect the appearance of intensity edges that are distant from edges in the previous frame. A global motion computation is used to handle camera or object motion. The algorithm we propose withstands JPEG and MPEG artifacts, even at very high compression rates. Experimental evidence demonstrates that our method can detect and classify scene breaks that are di cult to detect with previous approaches. An initial implementation runs at approximately 2 frames p e r second on a Sun workstation.

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“…AGC produces better contrast by effectively classifying and transforming the pixel intensities using Eqs. (2), (4), and (9).…”

Section: Qualitative Assessmentmentioning

confidence: 99%

“…In many cases, these images might demand enhancement for making them more acceptable to the common people. Furthermore, image enhancement is needed because of its wide range of application in areas such as atmospheric sciences [2], astrophotography [3], medical image processing [4], satellite image analysis [5], texture analysis and synthesis [6], remote sensing [7], digital photography, surveillance [8], and video processing applications [9].…”

Section: Introductionmentioning

confidence: 99%

An adaptive gamma correction for image enhancement

Rahman

Abdullah-Al-Wadud

et al. 2016

J Image Video Proc.

253

123

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Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital images may get degraded. In spite of much advancement in imaging science, captured images do not always fulfill users' expectations of clear and soothing views. Most of the existing methods mainly focus on either global or local enhancement that might not be suitable for all types of images. These methods do not consider the nature of the image, whereas different types of degraded images may demand different types of treatments. Hence, we classify images into several classes based on the statistical information of the respective images. Afterwards, an adaptive gamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of AGC are set dynamically based on the image information. Extensive experiments along with qualitative and quantitative evaluations show that the performance of AGC is better than other state-of-the-art techniques.

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Image processing on compressed data for large video databases

Cited by 165 publications

References 15 publications

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

A feature-based algorithm for detecting and classifying production effects

An adaptive gamma correction for image enhancement

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