A neural synaptic compression codec for efficient image transmission

Abstract

For efficient cellular communication channel usage, we propose a neural computation model for image coding. In a constant-time unsupervised learning, our neural model approximates optimal pattern clustering from training example images through a memory adaptation process, and builds a compression codebook in its synaptic weight matrix. This neural codebook can be distributed to both ends of a transmission channel for fast codec operations on general images. The transmission is merely the indices of the codebook entries best matching the patterns in the image to be transmitted. These indices can further be compressed through a classical entropy coding method to yield even more transmission reduction. Other advantages of our model are the low training time complexity, high utilization of neurons, robust pattern clustering capability, and simple computation. A VLSI implementation is also highly suitable for the intrinsic parallel nature of neural networks. Our compression results are competitive compared to JPEG and wavelet methods. We also reveal the general codebook's cross-compression results, filtering effects by special training methods, and learning enhancement techniques for obtaining a compact codebook to yield both high compression and picture quality.