Parallel Analog Image Coding and Decoding by Using Cellular Neural Networks

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概要

• 論文の詳細を見る

• This paper describes highly parallel analog image coding and decoding by cellular neural networks (CNNs). The communication system in which the coder (C-) and decoder (D-) CNNs are embedded consists of a differential transmitter with an internal receiver model in the feedback loop. The C-CNN encodes the image through two cascaded techniques: structural compression and halftoning. The D-CNN decodes the received data through a reconstruction process, which includes a dynamic current distribution, so that the original input to the C-CNN can be recognized. The halftoning serves as a dynamic quantization to convert each pixel to a binary value depending on the neighboring values. We approach halftoning by the minimization of error energy between the original gray image and reconstructed halftone image, and the structural compression from the viewpoints of topological and regularization theories. All dynamics are described by CNN state equations. Both the proposed coding and decoding algorithms use only local image information in a space invariant manner, therefore errors are distributed evenly and will not introduce the blocking effects found in DCT-based coding methods. In the future, the use of parallel inputs from on-chip photodetectors would allow direct dynamic quantization and compression of image sequences without the use of multiple bit analog-to-digital converters. To validate our theory, a simulation has been performed by using the relaxation method on an 150 frame image sequence. Each input image was 256× pixels with 8 bits per pixel. The simulated fixed compression rate, not including the Huffman coding, was about 1/16 with a PSNR of 31[dB]〜35[dB].

• 社団法人電子情報通信学会の論文
• 1994-08-25

著者

• ROSKA Tamas

  Computer and Automation Institute, Hungarian Academy of Sciences

• Roska Tamas

  Computer And Automation Institute Hungarian Academy Of Sciences

• Tanaka Mamoru

  Faculty of Science and Technology, Sophia University

• Crounse Kenneth

  Department of Electrical Engineering and Computer Science, University of California at Berkeley

• Crounse Kenneth

  Department Of Electrical Engineering And Computer Science University Of California At Berkeley

• Tanaka Mamoru

  Faculty Of Science And Technology Sophia University

関連論文

• Detecting Lung Cancer Symptoms with Analogic CNN Algorithms Based on a Constrained Diffusion Template
• Pipelining Gauss Seidel Method for Analysis of Discrete Time Cellular Neural Networks
• Chaotic Behavior in Simple Looped MOS Inverters
• Parallel Analog Image Coding and Decoding by Using Cellular Neural Networks
• Discrete Time Cellular Neural Networks with Two Types of Neuron Circuits for Image Coding and Their VLSI Implementations

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