Comparison of prediction methods for differential image processing applications

IEEE 1991 International Conference on Systems Engineering Pub Date : 1900-01-01 DOI:10.1109/ICSYSE.1991.161115

S. Chakravarti, T. Jung, S. Ahalt, A. Krishnamurthy

引用次数: 10

Abstract

An overview of ongoing research related to the development of an image data compression algorithm using artificial neural networks (ANNs) is presented. The data compression technique under study uses an ANN to perform vector quantization (VQ). A good predictor is one of the essential components of the image compression technique being explored. The performance of the various predictors are compared including an average predictor, a median predictor, a recurrent artificial neural network (RANN) predictor, and a second-order optimal linear predictor. It is shown that, for some cases, a relatively simple recurrent artificial neural network predictor performs close to the second-order optimal linear predictor and better than the average and the median predictors.<>

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差分图像处理中预测方法的比较

概述了使用人工神经网络(ANNs)开发图像数据压缩算法的正在进行的研究。所研究的数据压缩技术使用人工神经网络进行矢量量化(VQ)。一个好的预测器是正在探索的图像压缩技术的基本组成部分之一。比较了各种预测器的性能，包括平均预测器、中位数预测器、循环人工神经网络(RANN)预测器和二阶最优线性预测器。结果表明，在某些情况下，相对简单的递归人工神经网络预测器的性能接近于二阶最优线性预测器，并且优于平均值和中位数预测器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE 1991 International Conference on Systems Engineering

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