Color image compression and limited display using self-organization Kohonen map

Pei, Soo‐Chang; Lo, You-Shen

doi:10.1109/76.664104

Cited by 46 publications

(2 citation statements)

References 21 publications

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“…For this reason, both d and d 2 lead to the same result in the process of identifying the winning neuron. Moreover, measure d 2 is often favored over measure d because it allows omitting the rooting operation and therefore decreases the computational complexity of the SOM algorithm [22, 23].…”

Section: Functional Description Of Ssom Architecturementioning

confidence: 99%

“…To validate the SSOM, we propose the image compression based on the use of SOMs as an application. Indeed, the use of these networks for compression has been widely exploited in the literature [13, 16–18, 22–24]. Most of these models have been executed in three phases: color quantification, compressed image generation, and image reconstruction.…”

Section: Application: Image Compression For Ssom Validationmentioning

confidence: 99%

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A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network

Khalifa

Blaiech

Bedoui

2019

Computational Intelligence and Neuroscience

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In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is formed by two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. The NP modules have an innovative architecture compared to those proposed in the literature. Indeed, each NP performs three different tasks without requiring additional external modules. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature.

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Section: Functional Description Of Ssom Architecturementioning

confidence: 99%