Ann Dooms

A Locally Adaptive System for the Fusion of Objective Quality Measures

Objective measures to automatically predict the perceptual quality of images or videos can reduce the time and cost requirements of end-to-end quality monitoring. For reliable quality predictions, these objective quality measures need to respond consistently with the behavior of the human visual system (HVS). In practice, many important HVS mechanisms are too complex to be modeled directly. Instead, they can be mimicked by machine learning systems, trained on subjective quality assessment databases, and applied on predefined objective quality measures for specific content or distortion classes. On the downside, machine learning systems are often difficult to interpret and may even contradict the input objective quality measures, leading to unreliable quality predictions. To address this problem, we developed an interpretable machine learning system for objective quality assessment, namely the locally adaptive fusion (LAF). This paper describes the LAF system and compares its performance with traditional machine learning. As it turns out, the LAF system is more consistent with the input measures and can better handle heteroscedastic training data.

Bayesian crack detection in ultra high resolution multimodal images of paintings

The preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of art. In this paper we propose a semi-supervised crack detection method that can be used for high-dimensional acquisitions of paintings coming from different modalities. Our dataset consists of a recently acquired collection of images of the Ghent Altarpiece (1432), one of Northern Europes most important art masterpieces. Our goal is to build a classifier that is able to discern crack pixels from the background consisting of non-crack pixels, making optimal use of the information that is provided by each modality. To accomplish this we employ a recently developed non-parametric Bayesian classifier, that uses tensor factorizations to characterize any conditional probability. A prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors. The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm.

Robust Image Content Authentication with Tamper Location

We propose a novel image authentication system by combining perceptual hashing and robust watermarking. An image is divided into blocks. Each block is represented by a compact hash value. The hash value is embedded in the block. The authenticity of the image can be verified by re-computing hash values and comparing them with the ones extracted from the image. The system can tolerate a wide range of incidental distortion, and locate tampered areas as small as 1/64 of an image. In order to have minimal interference, we design both the hash and the watermark algorithms in the wavelet domain. The hash is formed by the sign bits of wavelet coefficients. The lattice-based QIM watermarking algorithm ensures a high payload while maintaining the image quality. Extensive experiments confirm the good performance of the proposal, and show that our proposal significantly outperforms a state-of-the-art algorithm.

Multispectral imaging for digital painting analysis: a Gauguin case study

This paper is an introduction into the analysis of multispectral recordings of paintings. First, we will give an overview of the advantages of multispectral image analysis over more traditional techniques: first of all, the bands residing in the visible domain provide an accurate measurement of the color information which can be used for analysis but also for conservational and archival purposes (i.e. preserving the art patrimonial by making a digital library). Secondly, inspection of the multispectral imagery by art experts and art conservators has shown that combining the information present in the spectral bands residing in- and outside the visible domain can lead to a richer analysis of paintings. In the remainder of the paper, practical applications of multispectral analysis are demonstrated, where we consider the acquisition of thirteen different, high resolution spectral bands. Nine of these reside in the visible domain, one in the near ultraviolet and three in the infrared. The paper will illustrate the promising future of multispectral analysis as a non-invasive tool for acquiring data which cannot be acquired by visual inspection alone and which is highly relevant to art preservation, authentication and restoration. The demonstrated applications include detection of restored areas and detection of aging cracks.

Design of an H.264/SVC resilient watermarking scheme

The rapid dissemination of media technologies has lead to an increase of unauthorized copying and distribution of digital media. Digital watermarking, i.e. embedding information in the multimedia signal in a robust and imperceptible manner, can tackle this problem. Recently, there has been a huge growth in the number of different terminals and connections that can be used to consume multimedia. To tackle the resulting distribution challenges, scalable coding is often employed. Scalable coding allows the adaptation of a single bit-stream to varying terminal and transmission characteristics. As a result of this evolution, watermarking techniques that are robust against scalable compression become essential in order to control illegal copying. In this paper, a watermarking technique resilient against scalable video compression using the state-of-the-art H.264/SVC codec is therefore proposed and evaluated.

Forensic data hiding optimized for JPEG 2000

This paper presents a novel image adaptive data hiding system using properties of the discrete wavelet transform and which is ready to use in combination with JPEG 2000. Image adaptive watermarking schemes determine the embedding samples and strength from the image statistics. We propose to use the energy of wavelet coefficients at high frequencies to measure the amount of distortion that can be tolerated by a lower frequency coefficient. The watermark decoder in image adaptive data hiding needs to estimate the same parameters used for encoding from a modified source and hence is vulnerable to desynchronization. We present a novel way to resolve these synchronization issues by employing specialized insertion, deletion and substitution codes. Given the low complexity and reduced perceptual impact of the embedding technique, it is suitable for inserting camera and/or projector information to facilitate image forensics.

Labelling bins for lattice quantization index modulation

In this paper, the effect of binary labelling bins for lattice quantization index modulation techniques is studied. The problem can be solved in one dimension using Gray codes but it is not straightforward for higher dimensions due to more directionality. We show the impact of different labellings on the overall performance of two-dimensional lattice quantization index modulation watermarking systems. Heuristically, we present solutions for these systems, where our analysis includes (1) robustness tests against JPEG and JPEG 2000 compression and (2) transmission over an AWGN channel.

Design and evaluation of sparse Quantization Index Modulation watermarking schemes

In the past decade the use of digital data has increased significantly. The advantages of digital data are, amongst others, easy editing, fast, cheap and cross-platform distribution and compact storage. The most crucial disadvantages are the unauthorized copying and copyright issues, by which authors and license holders can suffer considerable financial losses. Many inexpensive methods are readily available for editing digital data and, unlike analog information, the reproduction in the digital case is simple and robust. Hence, there is great interest in developing technology that helps to protect the integrity of a digital work and the copyrights of its owners. Watermarking, which is the embedding of a signal (known as the watermark) into the original digital data, is one method that has been proposed for the protection of digital media elements such as audio, video and images. In this article, we examine watermarking schemes for still images, based on selective quantization of the coefficients of a wavelet transformed image, i.e. sparse quantization-index modulation (QIM) watermarking. Different grouping schemes for the wavelet coefficients are evaluated and experimentally verified for robustness against several attacks. Wavelet tree-based grouping schemes yield a slightly improved performance over block-based grouping schemes. Additionally, the impact of the deployment of error correction codes on the most promising configurations is examined. The utilization of BCH-codes (Bose, Ray-Chaudhuri, Hocquenghem) results in an improved robustness as long as the capacity of the error codes is not exceeded (cliff-effect).

Feature point based image watermarking with insertions, deletions, and substitution codes

In this paper we concentrate on robust image watermarking (i.e. capable of resisting common signal processing operations and intentional attacks to destroy the watermark) based on image features. Kutter et al.7 motivated that well chosen image features survive admissible image distortions and hence can benefit the watermarking process. These image features are used as location references for the region in which the watermark is embedded. To realize the latter, we make use of previous work16 where a ring-shaped region, centered around an image feature is determined for watermark embedding. We propose to choose a specific sequence of image features according to strict criteria so that the image features have large distance to other chosen image features so that the ring shaped embedding regions do not overlap. Nevertheless, such a setup remains prone to insertion, deletion and substitution errors. Therefore we applied a two-step coding scheme similar to the one employed by Coumou and Sharma4 for speech watermarking. Our contribution here lies in extending Coumou and Sharmas one dimensional scheme to the two dimensional setup that is associated with our watermarking technique. The two-step coding scheme concatenates an outer Reed-Solomon error-correction code with an inner, blind, synchronization mechanism.