Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dyna...Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.展开更多
High efficiency audio compression is the basic technology in audio involved multimedia communications. Downmixing and parametric coding is efficient coding scheme with wide applications in some up-to-date audio codecs...High efficiency audio compression is the basic technology in audio involved multimedia communications. Downmixing and parametric coding is efficient coding scheme with wide applications in some up-to-date audio codecs such as Parametric Stereo (PS) in EAAC+ and MPEG-Surround. Principle Component Analysis (PCA) stereo coding followed this idea to map two channels to one channel with maximum energy and parameterize the secondary channel. This paper investigates the conventional PCA method performance under general stereo model with multiple sound sources and different directions, and then proposes a Polar Coordinate based PCA (PC-PCA) stereo coding method. It has been proved that when multiple sound sources exist with different directions, PC-PCA is better than the conventional PCA method when Mean to Standard deviation Ratio (MSR) is large. A stereo codec based on PC-PCA is proposed to validate the performance improvement of proposed method. Objective and subjective tests show the proposed method achieves a comparative quality and saves 50% parameter bit rate comparing with conventional PCA method, and obtains a 4-8 MUSHRA scores improvement comparing with state-of-the-art stereo codec at the same parameter bit rate.展开更多
基金supported by the Innovation Project of Graduate Students of Jiangsu Province, China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240, No. 61201160, No. 61172118+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 12KJB510019the Science and Technology Research Program of Hubei Provincial Department of Education under Grants No. D20121408, No. D20121402the Program for Research Innovation of Nanjing Institute of Technology Project under Grant No. CKJ20110006
文摘Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.
基金supported by National Natural Science Foundation of China under Grants No. 61231015, No. 61102127 No. 61201340, No. 61201169Major National Science and Technology Special Projects under Grant No. 2010ZX03004-003-03+2 种基金Natural Science Foundation of Hubei Province under Grant No. 2011CDB451Wuhan ChenGuang Science and Technology Plan under Grant No. 201150431104the Fundamental Research Funds for the Central Universities
文摘High efficiency audio compression is the basic technology in audio involved multimedia communications. Downmixing and parametric coding is efficient coding scheme with wide applications in some up-to-date audio codecs such as Parametric Stereo (PS) in EAAC+ and MPEG-Surround. Principle Component Analysis (PCA) stereo coding followed this idea to map two channels to one channel with maximum energy and parameterize the secondary channel. This paper investigates the conventional PCA method performance under general stereo model with multiple sound sources and different directions, and then proposes a Polar Coordinate based PCA (PC-PCA) stereo coding method. It has been proved that when multiple sound sources exist with different directions, PC-PCA is better than the conventional PCA method when Mean to Standard deviation Ratio (MSR) is large. A stereo codec based on PC-PCA is proposed to validate the performance improvement of proposed method. Objective and subjective tests show the proposed method achieves a comparative quality and saves 50% parameter bit rate comparing with conventional PCA method, and obtains a 4-8 MUSHRA scores improvement comparing with state-of-the-art stereo codec at the same parameter bit rate.
