Browsing by Author "de Queiroz, Ricardo"

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    Compression Color Space Estimation of JPEG Images using Lattice Basis Reduction
    (2001-10-01) Neelamani, Ramesh; Baraniuk, Richard G.; de Queiroz, Ricardo; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    Given a color image that was previously JPEG-compressed in some hidden color space, we aim to estimate this unknown compression color space from the image. This knowledge is potentially useful for color image enhancement and JPEG recompression. JPEG operates on the discrete cosine transform (DCT) coefficients of each color plane independently during compression. Consequently, the DCT coefficients conform to a lattice structure. We exploit this special geometry using the lattice reduction algorithm from number theory and cryptography to estimate the compression color space. Simulations verify that the proposed algorithm yields accurate compression color space estimates.
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    JPEG Compression History Estimation for Color Images
    (2006) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Dash, Sanjeeb; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    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 maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind 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.
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    JPEG Compression History Estimation for Color Images
    (2006-06-01) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settingsâ termed its JPEG compression history (CH)â 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 maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind 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 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.
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    JPEG Compression History Estimation for Color Images
    (2003-09-01) Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We routinely encounter digital color images that were previously JPEG-compressed. We aim to retrieve the various settings - termed JPEG compression history (CH) - employed during previous JPEG operations. This information is often discarded en-route to the image's current representation. The discrete cosine transform coefficient histograms of previously JPEG-compressed images exhibit near-periodic behavior due to quantization. We propose a statistical approach to exploit this structure and thereby estimate the image's CH. Using simulations, we first demonstrate the accuracy of our estimation. Further, we show that JPEG recompression performed by exploiting the estimated CH strikes an excellent file-size versus distortion tradeoff.
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    Lattice Algorithms for Compression Color Space Estimation in JPEG Images
    (2001-08-01) Neelamani, Ramesh; de Queiroz, Ricardo; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    JPEG (Joint Photographic Experts Group) is an international standard to compress and store digital color images [5]. Given a color image that was previously JPEG-compressed in some hidden color space, we aim to estimate this unknown compression color space from the image. This knowledge is potentially useful for color image enhancement and JPEG re-compression. JPEG operates on the discrete cosine transform (DCT) coefficients of each color plane independently during compression. Consequently, the DCT coefficients of the color image conform to a lattice structure. We exploit this special geometry using the lattice reduction algorithm from number theory and cryptography to estimate the compression color space. Simulations verify that the proposed algorithm yields accurate compression color space estimates.