A novel content-aware interleaving for wireless video transmission

Choi, Jaehyuk; Shin, Jitae

doi:10.1016/j.comcom.2006.01.022

Cited by 4 publications

(1 citation statement)

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“…Nevertheless, the use of such techniques is unsuitable in many applications for which energy, time and/or bandwidth (among others) are considered as very scarce resources. For those cases, even if unable to perform error correction, interleaving methods have proven to be effective on the reduction of the impact of network errors [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication

Zapata-Quiñones

Durán-Faúndez

Gutiérrez

et al. 2017

Sensors

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Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges.

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Section: Introductionmentioning

confidence: 99%