Applications of Image Compression on Agricultural Image Data Analysis

Abstract

With the advent of the cutting-edge technologies in information repository and communication, data storage is rapidly increased, and it is required to reduce the size of the data. Especially in the case of agricultural image data like types of plants, crops, seeds; kinds of diseases and their remedial pesticides; and the agricultural satellite; images require a huge volume of memory space to store. To avoid this problem, it is required to reduce the size of the data and redundancy of the data. To overcome this problem, this chapter proposes a compression method, based on an adaptive Gaussian Markov random field model for agricultural image data compression, where the images are assumed to be a Gaussian Markov random field. The parameters of the model are estimated, based on the Bayesian approach. The authors use arithmetic coding to store seed values and parameters of the model as it augments the compression ratio. They also have studied the use of the M-H algorithm, which updates the parameters and through which the image contents such as untexturedness are captured.