Joint Compression and Classification for Textures in the Wavelet and Ridgelet Domain

Image Compression is a widely addressed research area. Many compression standards are in place. There are many methods for image classification. But the joint compression and classification is a new research area wherein the classification is attempted in the compressed domain. The joint compression and classification (JCC) is explored in wavelet domain by some researchers. But it is not yet explored in Ridgelet domain. This paper discusses the performance of JCC for Wavelet and Ridgelet domain for Texture images. The experimentation is done with objective analysis and subjective analysis. Objective analysis is performed using the Compression metrics-RMSE, PSNR and classification metric- CCR. Subjective analysis is performed using Human Visual Perception. It is found that the Ridgelet Transform gives less Mean Squared Error (MSE) and is better for Joint Compression and Classification of Texture images. Extensive experimentation has been carried out to arrive at the conclusion.