Progressive image transmission based on image spatio-temporal decomposition by sigma-delta cellular neural network

Aomori, Hisashi; Mizutani, Ryohei; Toda, Hideharu; Otake, Tsuyoshi

doi:10.1587/nolta.13.264

Cited by 1 publication

(12 citation statements)

References 7 publications

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“…The decoder reconstructs the input image via a cumulative luminance integrator from received binary images. Experimental results support that the proposed method can achieve completely lossless reconstruction for the Kodak dataset and the WED dataset, and transmission efficiency is drastically improved compared with [13].…”

Section: Introductionmentioning

confidence: 52%

“…In our previous work [13], we proposed a progressive image transmission framework by the sigmadelta cellular neural network (SD-CNN) [14]. This framework mimics the processes by which a scenery input to the retina is encoded in a sequence of a spatio-temporal spike.…”

Section: Introductionmentioning

confidence: 99%

“…Also, since light cannot penetrate through the walls, a VLC can ensure wireless communication security [19]. Therefore, [13] has the potential to realize not only progressive image transmission by a neuromorphic algorithm, but also direct image transmission and secret communication of images via a VLC. However, [13] has issues with transmission efficiency, which is defined as the number of binary images required to obtain a given quality reproduced image.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, [13] has the potential to realize not only progressive image transmission by a neuromorphic algorithm, but also direct image transmission and secret communication of images via a VLC. However, [13] has issues with transmission efficiency, which is defined as the number of binary images required to obtain a given quality reproduced image. This method achieves progressive image transmission for the Kodak dataset with visually lossless quality, but it requires as many as 256 binary images.…”

Section: Introductionmentioning

confidence: 99%

“…This method achieves progressive image transmission for the Kodak dataset with visually lossless quality, but it requires as many as 256 binary images. For higher speed transmission, transmission efficiency of [13] should be further improved.…”

Section: Introductionmentioning

confidence: 99%

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Image sequence decomposition via sigma-delta cellular neural network having coupled cells

Nakashima

Iriyama

Otake

et al. 2023

NOLTA

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In this paper, an image sequence decomposition by the sigma-delta cellular neural network (SD-CNN) with coupled cells and its composition framework is proposed. The SD-CNN, having coupled cells inspired by the second-order sigma-delta modulator, is employed for image sequence decomposition, and it enables effective image decomposition by the effects of complex dynamics. In our method, pixel luminance is described by the number of spikes within a discrete time window that corresponds to the number of SD-CNN iterations. For efficient image decomposition, a high luminance which requires a long time window, should be compensated. To solve this problem, we introduce the cumulated luminance integrator enables compensating a high luminance even with a short time window. Experimental results on various test images in the Kodak dataset and the Waterloo dataset support that all the test images can be mathematically lossless reconstructed from an image sequence decomposed via our method. It is also confirmed that the proposed method improves transmission efficiency for the Kodak dataset by approximately 80%, which is an issue with our previous method.

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Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%