Efficient transform-based texture image retrieval techniques under quantization effects

With the great demand for storing and transmitting images as well as their managing, the retrieval of compressed images is a field of intensive research. While most of the works have been devoted to the case of losslessly encoded images (by extracting features from the unquantized transform coefficients), new studies have shown that lossy compression has a negative impact on the performance of conventional retrieval systems. In this work, we investigate three different quantization schemes and propose for each one an efficient retrieval approach. More precisely, the uniform quantizer, the moment preserving quantizer and the distribution preserving quantizer are considered. The inherent properties of each quantizer are then exploited to design an efficient retrieval strategy, and hence, to reduce the drop of retrieval performances resulting from the quantization effect. Experimental results, carried out on three standard texture databases and a color dataset, show the benefits which can be drawn from the proposed retrieval approaches.