Differential pulse code modulation image compression using artifical neural networks

Majid Rabbani; Soheil A. Dianat

doi:10.1117/12.143233

8 April 1993 Differential pulse code modulation image compression using artifical neural networks

Majid Rabbani, Soheil A. Dianat

Proceedings Volume 1903, Image and Video Processing; (1993) https://doi.org/10.1117/12.143233
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

Differential pulse code modulation (DPCM) is a widely used technique for both lossy and lossless compression of images. In this paper, the effect of using a nonlinear predictor based on artificial neural networks (ANN) for a DPCM encoder is investigated. The ANN predictor uses a 3-layer perceptron model with 3 input nodes, 30 hidden nodes, and 1 output node. The back-propagation learning algorithm is used for the training of the network. Simulation results are presented to compare the performance of the proposed ANN-based nonlinear predictor with that of a global linear predictor as well as an optimized minimum-mean-squared-error (MMSE) linear predictor. Preliminary computer simulations demonstrate that for a typical test image, the zeroth-order entropy of the differential (error) image can be reduced by more than 15% compared to the case where optimum linear predictors are employed. Some future research directions are also discussed.

Citation Download Citation

Majid Rabbani and Soheil A. Dianat "Differential pulse code modulation image compression using artifical neural networks", Proc. SPIE 1903, Image and Video Processing, (8 April 1993); https://doi.org/10.1117/12.143233

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