Video Compression Algorithm Based on Neural Network Structures

Knop, Michał; Cierniak, Robert; Shah, Nimit D.

doi:10.1007/978-3-319-07173-2_61

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…A histogram of brightness changes slightly for motion scenes that take place on the same background. However, for scenes of gradual transition or cuts, it changes gradually or abruptly [18].…”

Section: Correlation Coefficient Methodsmentioning

confidence: 99%

“…(2) is compared with a threshold. If the correlation value is lower than the assumed threshold, the algorithm determines a new key frame [18].…”

Section: Correlation Coefficient Methodsmentioning

confidence: 99%

“…The process of matching two consecutive frames is a challenging issue. This problem can be solved in many ways, such as histogram based methods [8] [27], the correlation of images [18] [29] or entropy [17]. However, most algorithms are sensitive to noise and movements of the camera or objects.…”

Section: Introductionmentioning

confidence: 99%

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Video Key Frame Detection Based on SURF Algorithm

Grycuk

Knop

Mandal

2015

Lecture Notes in Computer Science

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Abstract. In this paper we present a new method for key frame detection. Our approach is based on a well known algorithm: Speeded-Up Robust Features (SURF), which is a crucial step of our method. The frames are compared by a SURF matcher, which allows to count the corresponding keypoints. The proposed method provides better results for professional and high resolution videos. The simulations we conducted proved the effectiveness of our approach. The algorithm requires only one input parameter.

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Section: Correlation Coefficient Methodsmentioning

confidence: 99%

“…(2) is compared with a threshold. If the correlation value is lower than the assumed threshold, the algorithm determines a new key frame [18].…”

Section: Correlation Coefficient Methodsmentioning

confidence: 99%

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Video Key Frame Detection Based on SURF Algorithm

Grycuk

Knop

Mandal

2015

Lecture Notes in Computer Science

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“…More efficient methods divide video into scenes with similar frames and are commonly based on key frame detection algorithms [3][4][5]. Key frame detection is a challenging issue that is solved in various ways, such as histogram-based methods, entropy analysis, and correlation of images [6][7][8]. Unfortunately, most methods are sensitive to movement of objects in the scene and noise.…”

Section: Introductionmentioning

confidence: 99%

Video key frame detection based on the restricted Boltzmann machine

Knop¹,

Kapuściński²,

Mleczko³

2015

J. Appl. Math. Comput. Mech.

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Abstract. In this paper we present a new method for key frame detection. Our approach is based on a well-known algorithm of the Restricted Boltzmann Machine (RBM), which is a pivotal step in our method. The frames are compared to the RBM matcher, which allows one to search for key frame in the video sequence. The Restricted Boltzmann Machine is one of sophisticated types of neural networks, which can process the probability distribution, and is applied to filtering image recognition and modelling. The learning procedure is based on the matrix description of RBM, where the learning samples are grouped into packages, and represented as matrices. Our research confirms a potential usefulness for video key frame detection. The proposed method provides better results for professional and high-resolution videos. The simulations we conducted proved the effectiveness of our approach. The algorithm requires only one input parameter.

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