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An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding / Fan Zhao in Signal processing. Image communication, Vol. 25 N° 9 (Octobre 2010) 

Public ISBD [article]  in Signal processing. Image communication > Vol. 25 N° 9 (Octobre 2010) . - pp. 697–708 Titre : An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding Type de document : texte imprimé Auteurs : Fan Zhao, Auteur ; Guizhong Liu, Auteur ; Xing Wang, Auteur Année de publication : 2012 Article en page(s) : pp. 697–708 Note générale : Electronique Langues : Anglais (eng) Mots-clés : Hyperspectral  images  Compression  coding  H.264/AVC  Prediction  mode  Correlation  coefficients Résumé : In this paper, an efficient macroblock-based diverse and flexible prediction modes selection algorithm is proposed for coding hyperspectral images, which is inspired by the prediction scheme of H264/AVC. Here, different modes are specified for the corresponding macroblocks (16×16 pixel regions of a band) of hyperspectral images other than the whole band image using only one reference band image for prediction. Only the 4×4 mode is employed for the intra-band prediction in view of the fact that correlation coefficients of pixels separated by not more than four pixels in the spatial domain are greater than 0.65 at most cases. The optimal reference band is determined by the fast reference band selection algorithm; thereafter, the best partition of the candidate macroblock in the optimal reference band is further selected for inter-band prediction of the current macroblock. Thus, the stronger correlation in the spectral direction or in the spatial domain is utilized for the prediction of the given macroblock. With a comparably low memory requirement, the prediction coding scheme is proposed to speed up the implemental process using the fast reference band selection algorithm, the integer DCT and the quantization, which just needs the multiplication and bit-shifts operations. Several AVIRIS images are used to evaluate the performance of the algorithm. The proposed scheme outperforms the state-of-the-art 3D-based compression algorithms at lower rates. Moreover, compared with the method by using all the prediction modes of H.264/AVC, about 80% encoding time can be saved by our method under the same experimental condition. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596510000871 [article] An efficient macroblock-based diverse and flexible prediction modes selection for hyperspectral images coding [texte imprimé] / Fan Zhao, Auteur ; Guizhong Liu, Auteur ; Xing Wang, Auteur . - 2012 . - pp. 697–708. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 25 N° 9 (Octobre 2010) . - pp. 697–708 Mots-clés : Hyperspectral  images  Compression  coding  H.264/AVC  Prediction  mode  Correlation  coefficients Résumé : In this paper, an efficient macroblock-based diverse and flexible prediction modes selection algorithm is proposed for coding hyperspectral images, which is inspired by the prediction scheme of H264/AVC. Here, different modes are specified for the corresponding macroblocks (16×16 pixel regions of a band) of hyperspectral images other than the whole band image using only one reference band image for prediction. Only the 4×4 mode is employed for the intra-band prediction in view of the fact that correlation coefficients of pixels separated by not more than four pixels in the spatial domain are greater than 0.65 at most cases. The optimal reference band is determined by the fast reference band selection algorithm; thereafter, the best partition of the candidate macroblock in the optimal reference band is further selected for inter-band prediction of the current macroblock. Thus, the stronger correlation in the spectral direction or in the spatial domain is utilized for the prediction of the given macroblock. With a comparably low memory requirement, the prediction coding scheme is proposed to speed up the implemental process using the fast reference band selection algorithm, the integer DCT and the quantization, which just needs the multiplication and bit-shifts operations. Several AVIRIS images are used to evaluate the performance of the algorithm. The proposed scheme outperforms the state-of-the-art 3D-based compression algorithms at lower rates. Moreover, compared with the method by using all the prediction modes of H.264/AVC, about 80% encoding time can be saved by our method under the same experimental condition. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596510000871

A semantic framework for video genre classification and event analysis / Junyong You in Signal processing. Image communication, Vol. 25 N° 4 (Avril 2010) 

Public ISBD [article]  in Signal processing. Image communication > Vol. 25 N° 4 (Avril 2010) . - pp. 287–302 Titre : A semantic framework for video genre classification and event analysis Type de document : texte imprimé Auteurs : Junyong You, Auteur ; Guizhong Liu, Auteur ; Andrew Perkis, Auteur Année de publication : 2012 Article en page(s) : pp. 287–302 Note générale : Electronique Langues : Anglais (eng) Mots-clés : Semantic  video  analysis  Probabilistic  model  Video  genre  classification  Event  analysis Résumé : Semantic video analysis is a key issue in digital video applications, including video retrieval, annotation, and management. Most existing work on semantic video analysis is mainly focused on event detection for specific video genres, while the genre classification is treated as another independent issue. In this paper, we present a semantic framework for weakly supervised video genre classification and event analysis jointly by using probabilistic models for MPEG video streams. Several computable semantic features that can accurately reflect the event attributes are derived. Based on an intensive analysis on the connection between video genres and the contextual relationship among events, as well as the statistical characteristics of dominant event, a hidden Markov model (HMM) and naive Bayesian classifier (NBC) based analysis algorithm is proposed for video genre classification. Another Gaussian mixture model (GMM) is built to detect the contained events using the same semantic features, whilst an event adjustment strategy is proposed according to an analysis on the GMM structure and pre-definition of video events. Subsequently, a special event is recognized based on the detected events by another HMM. The simulative experiments on video genre classification and event analysis using a large number of video data sets demonstrate the promising performance of the proposed framework for semantic video analysis. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596510000172 [article] A semantic framework for video genre classification and event analysis [texte imprimé] / Junyong You, Auteur ; Guizhong Liu, Auteur ; Andrew Perkis, Auteur . - 2012 . - pp. 287–302. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 25 N° 4 (Avril 2010) . - pp. 287–302 Mots-clés : Semantic  video  analysis  Probabilistic  model  Video  genre  classification  Event  analysis Résumé : Semantic video analysis is a key issue in digital video applications, including video retrieval, annotation, and management. Most existing work on semantic video analysis is mainly focused on event detection for specific video genres, while the genre classification is treated as another independent issue. In this paper, we present a semantic framework for weakly supervised video genre classification and event analysis jointly by using probabilistic models for MPEG video streams. Several computable semantic features that can accurately reflect the event attributes are derived. Based on an intensive analysis on the connection between video genres and the contextual relationship among events, as well as the statistical characteristics of dominant event, a hidden Markov model (HMM) and naive Bayesian classifier (NBC) based analysis algorithm is proposed for video genre classification. Another Gaussian mixture model (GMM) is built to detect the contained events using the same semantic features, whilst an event adjustment strategy is proposed according to an analysis on the GMM structure and pre-definition of video events. Subsequently, a special event is recognized based on the detected events by another HMM. The simulative experiments on video genre classification and event analysis using a large number of video data sets demonstrate the promising performance of the proposed framework for semantic video analysis. ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science/article/pii/S0923596510000172