Improving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression System | Science Publications

Journal of Computer Science

Omaima N. A.¹ 

关键词: Image compression;    artificial neural networks;    backpropagation neural network;   

DOI  :  10.3844/jcssp.2010.1347.1354

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: The problem inherent to any digital image is the large amount ofbandwidth required for transmission or storage. This has driven the research area of imagecompression to develop algorithms that compress images to lower data rates with better quality.Artificial neural networks are becoming attractive in image processing where high computationalperformance and parallel architectures are required. Approach: In this research, a three layeredBackpropagation Neural Network (BPNN) was designed for building imagecompression/decompression system. The Backpropagation neural network algorithm (BP) was used fortraining the designed BPNN. Many techniques were used to speed up and improve this algorithm byusing different BPNN architecture and different values of learning rate and momentum variables.Results: Experiments had been achieved, the results obtained, such as Compression Ratio (CR) andpeak signal to noise ratio (PSNR) are compared with the performance of BP with different BPNNarchitecture and different learning parameters. The efficiency of the designed BPNN comes fromreducing the chance of error occurring during the compressed image transmission through analog ordigital channel. Conclusion: The performance of the designed BPNN image compression system canbe increased by modifying the network itself, learning parameters and weights. Practically, we cannote that the BPNN has the ability to compress untrained images but not in the same performance ofthe trained images.

【 授权许可】

Unknown