JPEG Compression History Estimation for Color Images

R. Neelamani, R. de Queiroz, Z. Fan, S. Dash, and R. G. Baraniuk

(IEEE Transactions on Image Processing, To appear, 2005)

We routinely encounter digital color images that were previously JPEG-compressed. En-route to the image's current representation, the previous JPEG compression's various settings---termed its JPEG compression history (CH)--are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the {\em maximum a posteriori} estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a {\em blind} lattice-based CHEst algorithm. This algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be extremely useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.

Support: This work was supported by the NSF grant CCR--99--73188, AFOSR grant F49620--01--1--0378, ONR grant R13820, DARPA grant R13360, and Texas Instruments.