The multiresolution wavelet transform is an effective procedure in texture analysis. However, many images are still compressed by the methods based on the discrete cosine transform (DCT). Thus, decompression of the inverse DCT is required to yield the textural features based on the wavelet transform for the DCT-coded image. This investigation adopts the multiresolution reordered features in texture analysis. The proposed features are directly generated using the DCT coefficients of the encoded image. Comparisons between the subband-energy features extracted from the wavelet transform and the conventional DCT, using the Brodatz texture database, demonstrate that the proposed method offers the best textural pattern retrieval accuracy and yields a much higher classification rate. The proposed DCT features are expected to be very useful and efficient in retrieving and classifying texture patterns in large DCT-coded image databases.