Feature selection for neural-network based no-reference video quality assessment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5769 LNCS, Issue PART 2, 2009, Pages 633-642

  Ćulibrk, Dubravko ^a   Kukolj, Dragan ^a   Vasiljević, Petar ^a   Pokrić, Maja ^a   Zlokolica, Vladimir ^a  

^a UNIVERSITY OF NOVI SAD   (Serbia)

Author keywords

Multi layer perceptron; Neural networks; No reference approach; Perceptual quality; Video quality assessment

Indexed keywords

BITRATES; CODING PROCESS; DESIGN OF ALGORITHMS; ESTIMATION ALGORITHM; FEATURE SELECTION; HUMAN OBSERVERS; HUMAN PERCEPTION; MEAN OPINION SCORES; MULTI LAYER PERCEPTRON; NO REFERENCES; OBJECTIVE MEASURE; OBJECTIVE QUALITY MEASURES; PERCEPTUAL QUALITY; QUALITY ASSESSMENT; STANDARD DEFINITIONS; STANDARDIZED PROCEDURE; VIDEO CODING ARTIFACTS; VIDEO CONTENTS; VIDEO QUALITY; VIDEO QUALITY ASSESSMENT; VIDEO STREAMS;

BACKPROPAGATION; DIFFUSERS (OPTICAL); FEATURE EXTRACTION; FLUORINE CONTAINING POLYMERS; IMAGE QUALITY; MOTION PICTURE EXPERTS GROUP STANDARDS; VIDEO SIGNAL PROCESSING; VISUAL COMMUNICATION;

NEURAL NETWORKS;

EID: 70450194120     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04277-5_64     Document Type: Conference Paper

References (14)

1. Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of jpeg compressed images, pp. 477-480 (2002)
2. Warwick, G., Thong, N.: Classification of Video Sequences in MPEG Domain. In: Signal Processing for Telecommunications and Multimedia, ch. 6, Springer, Heidelberg (2004)
3. Kirenko, I.: Reduction of coding artifacts using chrominance and luminance spatial analysis. In: International Conference on Consumer Electronics, ICCE 2006, Digest of Technical Papers, pp. 209-210 (2006)
4. Ferzli, R., Karam, L.: A no-reference objective image sharpness metric based on just-noticeable blur and probability summation. In: IEEE International Conference on Image Processing, 2007. ICIP 2007, vol. 3, III-445-III-448 (2007)
5. BT.500, I.R.: Methodology for the Subjective Assessment of the Quality of Television Pictures (2002)
6. Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)
7. Kusuma, T., Caldera, M., Zepernick, H.: Utilising objective perceptual image quality metrics for implicit link adaptation, IV: 2319-2322 (2004)
8. Venkatesh Babu, R., Perkis, A., Hillestad, O.I.: Evaluation and monitoring of video quality for uma enabled video streaming systems. Multimedia Tools Appl. 37(2), 211-231 (2008)
9. Idrissi, N., Martinez, J., Aboutajdine, D.: Selecting a discriminant subset of cooccurrence matrix features for texture-based image retrieval, pp. 696-703 (2005)
10. Kim, K., Davis, L.: A fine-structure image/video quality measure using local statistics, V: 3535-3538 (2004)
11. Babu, R., Perkis, A.: An hvs-based no-reference perceptual quality assessment of jpeg coded images using neural networks (2005)
12. ftp://ftp.crc.ca/crc/vqeg/TestSequences/Reference/
13. Wolf, S., Pinson, M.: Ntia report 02-392: Video quality measurement techniques. Technical report (Institute for Telecommunication Sciences)
14. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)