8 April 1993 Differential pulse code modulation image compression using artifical neural networks
Author Affiliations +
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Majid Rabbani, Soheil A. Dianat, "Differential pulse code modulation image compression using artifical neural networks", Proc. SPIE 1903, Image and Video Processing, (8 April 1993); doi: 10.1117/12.143233; https://doi.org/10.1117/12.143233


Lapped block decoding for vector quantization of images
Proceedings of SPIE (November 01 1992)
Compaction of color images with arithmetic coding
Proceedings of SPIE (November 01 1991)
Optimum Restoration Of Coded And Transmitted Images
Proceedings of SPIE (July 09 1976)
New fast VQ encoding algorithm for image compression
Proceedings of SPIE (April 08 1993)
Bounded-Error Coding Of Cosine Transformed Images
Proceedings of SPIE (December 28 1979)

Back to Top