Abstract

Khaled Mahar
jpeg compressed image retrieval based on statistical featuresblocks co-occurrence matrix
current trends in computerinternet use show a significant increase in the amount of images being distributedstored in a compressed format. the most popular compression format defined by joint picture expert group (jpeg) becomes the current de facto standard for image compression. this paper introduces a framework for image retrieval system that operates in the compressed domain of jpeg. compressed domain retrieval allows the calculation of image features,hence the image retrieval, to be performed without full decompression. the proposed algorithm extracts a feature vector generated from the discrete cosine transform (dct) coefficient of the compressed images. this feature vector not only includes statistical information of a block color, but also carries coherence information of neighboring blocks. due to the high dimensionality of the feature vector, principal component analysis (pca) is used to reduce the dimensionality by eliminating redundant dimensions. experimental results support the idea of the proposed algorithm in achieving good performance in terms of retrieval efficiencyeffectiveness with comparison to other common methods.