Download Advances in Multimedia Modeling: 13th International by Luisa M. Regueras, María Jesús Verdú, Rafael Mompó (auth.), PDF

By Luisa M. Regueras, María Jesús Verdú, Rafael Mompó (auth.), Tat-Jen Cham, Jianfei Cai, Chitra Dorai, Deepu Rajan, Tat-Seng Chua, Liang-Tien Chia (eds.)

ISBN-10: 3540694285

ISBN-13: 9783540694281

The quantity set LNCS 4351 and LNCS 4352 constitutes the refereed court cases of the thirteenth foreign Multimedia Modeling convention, MMM 2007, held in Singapore in January 2007.

Based on rigorous reviewing, this system committee chosen 123 rigorously revised complete papers of the most technical classes and 33 revised complete papers of 4 precise periods from a complete of 392 submissions for presentation in volumes.

The first quantity contains seventy six contributions. They comprise insurance of subject matters with regards to studying semantic thoughts, snap shots, photograph registration, matching and texture, human-computer interplay, monitoring and movement research, complex media coding and edition, media annotation, photograph and video coding, context-aware media modeling, multimedia databases, and picture processing.

The moment quantity collects eighty papers. They comprise assurance of issues relating to multimedia sign processing and communications, occasion detection, multimedia over P2P, content material, purposes, desktop imaginative and prescient, snapshot processing, multimedia sign processing and communications, photograph type and popularity, complicated media processing and safeguard, and ubiquitous

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Extra info for Advances in Multimedia Modeling: 13th International Multimedia Modeling Conference, MMM 2007, Singapore, January 9-12, 2007. Proceedings, Part II

Example text

D = D0 + α ⋅ e − R / β (6) In this equation, D0, α, β are parameters determined by the video sequence (quantizes, variance, and motion respectively). We can compute the visual information using this R-D function: R V (R) = ∫ D ( r ) dr − D ( R ) ⋅ R = α ⋅ [β − (R + β ) ⋅ e −R / β ] (7) r =0 As shown in the equation above, the upper bound of visual information is determined by the motion of the video sequence and the variance of the video frames. We can also get the contribution of bit rate to the visual information: φ (R) = dV 1 = ⋅ R ⋅ e −R / β β dR (8) Optimization of System Performance for DVC Applications 29 4 Rate Allocation and Validation We first use the concept of visual information in single-source video applications, in this case, we joint optimize the decoding quality and the network lifetime.

3 Scenario-Driven Decoding at the Client Side The client can exploit the scenarios annotated to the video streams for driving the energy-efficient decoding. Before actually decoding video streams on the client side, we first need to profile the client by building the scenario adaptation table (SAT), see Fig. 1. The SAT contains the appropriate frequency and voltage for each of the scenarios. These frequencies and voltages are determined in such a way that a frame belonging to the scenario still meets its deadline while minimizing the decoder’s energy consumption.

3 Scenario-Driven Decoding at the Client Side The client can exploit the scenarios annotated to the video streams for driving the energy-efficient decoding. Before actually decoding video streams on the client side, we first need to profile the client by building the scenario adaptation table (SAT), see Fig. 1. The SAT contains the appropriate frequency and voltage for each of the scenarios. These frequencies and voltages are determined in such a way that a frame belonging to the scenario still meets its deadline while minimizing the decoder’s energy consumption.

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