JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Convex Optimization
Distortion estimators for bitplane image coding
IEEE Transactions on Image Processing
Multiple sender distributed video streaming
IEEE Transactions on Multimedia
Analysis of video transmission over lossy channels
IEEE Journal on Selected Areas in Communications
Motion-compensated 3-D subband coding of video
IEEE Transactions on Image Processing
High performance scalable image compression with EBCOT
IEEE Transactions on Image Processing
Multiple Description Image Coding Based on Lagrangian Rate Allocation
IEEE Transactions on Image Processing
Optimal Motion Estimation for Wavelet Motion Compensated Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
State-of-the-Art and Trends in Scalable Video Compression With Wavelet-Based Approaches
IEEE Transactions on Circuits and Systems for Video Technology
Multiple description coding for SNR scalable video transmission over unreliable networks
Multimedia Tools and Applications
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Multiple description scalable coding based on T+2D wavelet decomposition structure is highly flexible for peer-to-peer (P2P) video streaming. Finding the optimal truncation point of each wavelet-decomposed code block (CB) within each description is an NP-hard problem (Akyol et al., 2007 [1]). For P2P video streaming, it is necessary to implement an efficient multiple description encoder with three attributes; ''adaptive'' (due to the time-varying capacity of the P2P network links and nodes), ''low-complexity'' (because of the low processing power of the receiving peer) with arbitrarily ''unbalanced descriptions'' (because of the unequal capacities of the different sending peers). To design a multiple description encoder with the above mentioned features, we propose a simple clustering algorithm for partitioning the CBs into a limited number of clusters. This simple and efficient clustering algorithm significantly reduces the size of redundancy-rate assignment matrix, such that one can find the optimal channel-aware cluster-level redundancy-rate assignment matrix using a low-complexity full search approach. This approach improves the decoding quality compared to the co-echelon adaptive frameworks (Akyol et al., 2007 [1]; Tillo et al., 2007 [8]) in which a non-optimal heuristic rate assignment pattern is used. Especially for the unbalanced P2P scenario (which is the usual case), the performance gain of the proposed approach over the one presented in Tillo et al. (2007) [8] (generating only balanced descriptions) is significant (0.95-3.0dB). In addition, the proposed clustering approach may be analytically represented by closed-form relations for low-complexity computation of the optimal encoding parameters. Our complexity analysis shows that the proposed approach requires 52-96% less computations compared to the framework in Akyol et al. (2007) [1]. Therefore, an efficient real-time post-encoding adaptation mechanism may be realized. The simulation results demonstrate that the adaptive proposed framework outperforms the approach presented in Akyol et al. (2007) [1] by (0.26-0.95dB) and the non-adaptive multiple description coding by (1.1-2.3dB).