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In content-based image retrieval (CBIR) application, a large amount of floating-point data is processed. Among various low-level features, color is an important feature and represented in the form of histogram. It is essential that features required to be coded in such a way that the storage space requirement is low and processing speed is high. In this paper, we propose an encoding approach using Golomb-Rice coding, which effectively codes the floating point bin values of the color histogram. The floating point values are converted into integer values using preprocessing steps. The encoded histogram is finally represented in the form of sparse matrix and XOR based bitwise comparison is used as similarity measure to calculate the distance between the encoded and query histogram in the feature space. Based on the number of 1's, the retrieved list is ranked and the relevant images are presented. This approach is tested in CBIR application and the precision of retrieval is encouraging compared to the original color histogram and the average bit length is very low besides having fast retrieval time

Block Encoding of Color Histogram for Content based Image Retrieval Applications