For steganalysis of JPEG images,features derived in the embedding domain appear to achieve a prefer-able performance. However,with the existing JPEG steganography,the minor changes due to the hid-den secret data are not easy to be explored directly from the quantized block DCT (BDCT) coefficients in that the energy of the carrier image is much larger than that of the hidden signal. In this paper,we present an improved calibration-based universal JPEG steganalysis,where the microscopic and macro-scopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image. All features in our method are generated from the difference signal be-tween the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image,or calculated as the difference between the signal extracted from test image and its correspond-ing macroscopic calibrated image. The extracted features will be more effective for our classification. Moreover,through using the Markov empirical transition matrices,both magnitude and sign dependen-cies along row scanning and column scanning patterns existed in intra-block and inter-block quantized BDCT coefficients are employed in our method. Experimental results demonstrate that our proposed scheme outperforms the best effective JPEG steganalyzers having been presented. For steganalysis of JPEG images, features derived in the embedding domain appear to achieve a prefer-able performance. However, with the existing JPEG steganography, the minor changes due to the hid-den secret data are not easy to be explored directly from the quantized block this, we present an improved calibration-based universal JPEG steganalysis, where the microscopic and macro-scopic calibrations are combined to calibrate the local and global distribution of the quantized BDCT coefficients of the test image. All features in our method are generated from the difference signal be-tween the quantized BDCT coefficients of the test image and its corresponding microscopic calibrated image, or calculated as the difference between the signal extracted from test image and its correspond-ing macroscopic calibrated image. The extracted features will be more effective for our clas sification. Moreover, through using the Markov empirical transition matrices, both magnitude and sign dependen-cies along row scanning and column scanning patterns were in intra-block and inter-block quantized BDCT coefficients are employed in our method. scheme outperforms the best effective JPEG steganalyzers having been presented.