Mohammad Kazemi

Sharif University of Technology, Teheran, Tehrān, Iran

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Publications (4)2.9 Total impact

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    ABSTRACT: Multiple description coding (MDC) is a technique where multiple streams from a source video are generated, each individually decodable and mutually refinable. MDC is a promising solution to overcome packet loss in video transmission over noisy channels, particularly for real-time applications in which retransmission of lost information is not practical. A problem with conventional MDC is that the achieved side distortion quality is considerably lower than single description coding (SDC) quality except at high redundancies which in turn leads to central quality degradation. In this paper, a new mixed layer MDC scheme is presented with no degradation in central quality, and providing better side quality (approximately as much as that of SDC) compared to conventional methods. Also, this property directly leads to higher average quality when delivering the video in lossy networks. For each discrete cosine transform coefficient, we generate two coefficients: base coefficient (BC) and enhancement coefficient which are combined together. When all descriptions are available, they are decomposed and decoded to achieve high quality video. When one description is not available, we use estimation to extract as much of the BC as possible from the received description. Simulation results show that the proposed scheme leads to an improved redundancy-rate-distortion performance compared to conventional methods. The algorithm is implemented in JM16.0 and its performance for two-description and four-description coding is verified by experiments.
    IEEE Transactions on Circuits and Systems for Video Technology 03/2012; · 1.82 Impact Factor
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    ABSTRACT: A lucky labeling of a graph G is a function ℓ:V(G)→N, such that for every two adjacent vertices v and u of G, ∑w∼vℓ(w)≠∑w∼uℓ(w) (x∼y means that x is joined to y). A lucky number of G, denoted by η(G), is the minimum number k such that G has a lucky labeling ℓ:V(G)→{1,…,k}. We prove that for a given planar 3-colorable graph G determining whether η(G)=2 is NP-complete. Also for every k⩾2, it is NP-complete to decide whether η(G)=k for a given graph G.
    Information Processing Letters 01/2012; 112:109-112. · 0.49 Impact Factor
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    ABSTRACT: Multiple Description Coding (MDC) is a technique where multiple streams from a source are generated, each individually decodable and mutually refinable. In this paper, a new Mixed Layer MDC (MLMDC) scheme is presented which achieves a higher side quality compared to conventional MDCs. The improved side performance leads to higher average video quality at the receiver in lossy networks. For each DCT coefficient, we generate two coefficients: Base Coefficient (BC) and Enhancement Coefficient (EC) which are combined together. When all descriptions are available, they are decomposed and decoded to achieve high quality video. When one description is not available, we use estimation to extract as much of the BC as possible from the received description. The algorithm is implemented in JM16.0 and its performance for twodescription and four-description coding is verified by experiments.
    Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011, 11-15 July, 2011, Barcelona, Catalonia, Spain; 01/2011
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    ABSTRACT: Multiple description coding (MDC) is one of the promising solutions for live video delivery over lossy networks. In this paper, we present a review of MDC techniques based on their application domain and we explain their functionality, with the objective of giving enough insight to designers to decide which MDC scheme is best suited for their specific application based on requirements such as standard compatibility, redundancy tunability, complexity, and extendibility to n-description coding. The focus is mainly on video sources but image-based algorithms applicable to video are considered as well. We also cover the well-known and important problem of drift and solutions to avoid it.
    Multimedia Systems · 0.60 Impact Factor