An efficient statistical-based retrieval approach for JPEG2000 compressed images

This paper deals with the problem of image retrieval when the database is presented in a compressed form, by using typically the JPEG2000 encoding scheme based on wavelet transform followed by an uniform scalar quantization. The state-of-the-art method aims at applying a preprocessing step before the feature extraction to reduce the difference in the compression qualities between the images. Our contribution consists in extracting robust features directly from the quantized coefficients. More precisely, assuming that the unquantized coefficients within a subband have a Laplacian distribution, we propose to estimate the distribution parameter from the quantized coefficients. Then, the estimated parameters of the whole subbands are used to build a salient feature for the indexing process. Experimental results show that the proposed retrieval approach significantly improves the state-of-the-art one.