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 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 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.

/pdf/jpeg-compression-history-estimation-for-color-images-43jtuynen4.pdf

JPEG compression history estimation for color images

In order to cater to the diversity of terminals and networks, efficient, and flexible adaptation of multimedia content in the delivery path to end consumers is required. To this end, it is necessary to associate the content with metadata that provides the relationship between feasible adaptation choices and various media characteristics obtained as a function of these choices. Furthermore, adaptation is driven by specification of terminal, network, user preference or rights based constraints on media characteristics that are to be satisfied by the adaptation process. Using the metadata and the constraint specification, an adaptation engine can take an appropriate decision for adaptation, efficiently and flexibly. MPEG-21 Part 7 entitled Digital Item Adaptation standardizes among other things the metadata and constraint specifications that act as interfaces to the decision-taking component of an adaptation engine. This paper presents the concepts behind these tools in the standard, shows universal methods based on pattern search to process the information in the tools to make decisions, and presents some adaptation use cases where these tools can be used.

Optimal adaptation decision-taking for terminal and network quality-of-service

A sliding window-based analysis is a prevailing mechanism for tampering localization in passive image authentication. It uses existing forensic detectors, originally designed for a full-frame analysis, to obtain the detection scores for individual image regions. One of the main problems with a window-based analysis is its impractically low localization resolution stemming from the need to use relatively large analysis windows. While decreasing the window size can improve the localization resolution, the classification results tend to become unreliable due to insufficient statistics about the relevant forensic features. In this paper, we investigate a multi-scale analysis approach that fuses multiple candidate tampering maps, resulting from the analysis with different windows, to obtain a single, more reliable tampering map with better localization resolution. We propose three different techniques for multi-scale fusion, and verify their feasibility against various reference strategies. We consider a popular tampering scenario with mode-based first digit features to distinguish between singly and doubly compressed regions. Our results clearly indicate that the proposed fusion strategies can successfully combine the benefits of small-scale and large-scale analyses and improve the tampering localization performance.

Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images

A system and method for generating a second reduced size digital image from a first digital image, the method including iteratively compressing the first digital image to an extent determined by a quality measure comprising at least a blockiness measure quantifying added artifactual edges along coding block boundaries of the second image and/or use of a quantization matrix generated by computing a weighted average of the quantization matrix of the first digital image and a scaled second quantization matrix.

Apparatus and methods for recompression of digital images

We study lattice bases where the angle between any basis vector and the linear subspace spanned by the other basis vectors is at least $\frac{\pi}{3}$ radians; we denote such bases as “nearly orthogonal.” We show that a nearly orthogonal lattice basis always contains a shortest lattice vector. Moreover, we prove that if the basis vector lengths are “nearly equal,” then the basis is the unique nearly orthogonal lattice basis up to multiplication of basis vectors by $\pm 1$. We also study random lattices generated by the columns of random matrices with $n$ rows and $m \leq n$ columns. We show that if $m \leq c\,n$, with $c \approx 0.071$, then the random matrix forms a nearly orthogonal basis for the random lattice with high probability for large $n$ and almost surely as $n$ tends to infinity. Consequently, the columns of such a random matrix contain the shortest vector in the random lattice. Finally, we discuss an interesting JPEG image compression application where nearly orthogonal lattice bases play an important role.

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On Nearly Orthogonal Lattice Bases and Random Lattices

We report a novel heuristic for requantizing JPEG images. The resulting images are generally smaller and often have improved perceptual image quality over a "blind" requantization approach, that is, one that does not consider the properties of the quantization matrices. The heuristic is supported by a detailed mathematical treatment which incorporates the well-known Laplacian distribution of the AC discrete cosine transform (DCT) coefficients with an analysis of the error introduced by requantization. We note that the technique is applicable to any image compression method which employs discrete cosine transforms and quantization.

/pdf/recompression-of-jpeg-images-by-requantization-hv9hd2bxdt.pdf

Recompression of JPEG images by requantization

We propose a new method for requantizing JPEG images, based on the well-known Laplacian distribution of the AC discrete cosine transform (DCT) coefficients and on an analysis of the error introduced by requantization. The resulting images have improved perceptual image quality over a “blind” requantization approach, that is, one that does not consider the properties of the quantization matrices.

/pdf/a-requantization-based-method-for-recompressing-jpeg-images-2gx7vwtckz.pdf

J.S. McMichael

Papers

Recompression of JPEG images by requantization

A requantization-based method for recompressing JPEG images