Psychovisual Research Articles

Content-Adaptive Robust Image Watermarking with Posterior HMM-Based Detector

Robustness and invisibility are two contrary constraints for robust invisible watermarking. Instead of the conventional strategy with human visual system (HVS) model, this paper presents a content-adaptive approach to further optimize the constraint between them. To reach this target, the entropy-based and integrated HVS (IHVS) based measures are constructed so as to adaptively choose the suitable components for watermark insertion and detection. Such a kind of scheme potentially gives rise to synchronization problem between the encoder and decoder under the framework of blind watermarking, which is then solved by incorporating the repeat-accumulate (RA) code with erasure and error correction. Moreover, a new hidden Markov model (HMM) based detector in wavelet domain is introduced to reduce the computation complexity and is further developed into a posterior one to avoid the transmission of HMM parameters with only a little sacrifice of detection performance. Experimental results show that the proposed algorithm can obtain considerable improvement in robustness performance with the same distortion as the traditional one.

Alpha stable modeling of human visual systems for digital halftoning in rectangular and hexagonal grids

Human visual system (HVS) modeling has become a critical component in the design of digital halftoning algorithms. Methods that exploit the characteristics of the HVS include the direct binary search (DBS) and optimized tone-dependent halftoning approaches. The spatial sensitivity of the HVS is low-pass in nature, reflecting the physiological characteristics of the eye. Several HVS models have been proposed in the literature, among them, the broadly used Näsänen’s exponential model, which was later shown to be constrained in shape. Richer models are needed to attain better halftone attributes and to control the appearance of undesired patterns. As an alternative, models based on the mixture of bivariate Gaussian density functions have been proposed. The mathematical characteristics of the HVS model thus play a key role in the synthesis of model-based halftoning. In this work, alpha stable functions, an elegant class of functions richer than mixed Gaussians, are exploited to design HVS models to be used in two different contexts: monochrome halftoning over rectangular and hexagonal sampling grids. In the two scenarios, alpha stable models prove to be more efficient than Gaussian mixtures, as they use less parameters to characterize the tails and bandwidth of the model. It is shown that a decrease in the model’s bandwidth leads to homogeneous halftone patterns, and conversely, models with heavier tails yield smoother textures. These characteristics, added to their simplicity, make alpha stable models a powerful tool for HVS characterization.

Optimum retrieval of watermark from wavelet significant coefficients

Watermark retrieval in wavelet domain has been proved to be more robust from significant coefficients than high absolute coefficients when a single copy of the watermark is embedded in the original data. The highest absolute coefficients refer to the coefficients with the highest absolute values in any selected band and significant coefficients refer to the coefficients with the highest significance factor with respect to their inter-band dependencies in a wavelet transformed image. The watermark energy can be maximised at each of the selected coefficient by quantising it to the maximum allowable level suggested by a human visual system model. However, as the attacks become very severe, a single copy of the watermark is not sufficient for correct retrieval. Hence, the authors propose a method of optimum retrieval of the watermark from multiple embedded copies under very severe attacks. The authors propose to use the Chair–Varshney decision fusion rule to decide each bit in the watermark instead of the majority rule for optimum watermark retrieval. Simulations are performed to show the superiority of the method with different numbers of watermark copies under various attacks. Extensive simulations are carried out to plot the receiver operating characteristics in order to compare the proposed method with the majority rule.

A new automated quality assessment algorithm for image fusion

Automated image quality assessment is highly desirable to evaluate the performance of various image fusion algorithms for night vision applications. In this paper we propose a perceptual quality evaluation method for image fusion which is based on human visual system (HVS) models. Our method assesses the image quality of a fused image using the following steps. First, the source and fused images are filtered by a contrast sensitivity function (CSF) after which a local contrast map is computed for each image. Second, a contrast preservation map is generated to describe the relationship between the fused image and each source image. Finally, the preservation maps are weighted by a saliency map to obtain an overall quality map. The mean of the quality map indicates the quality of the fused image. Experimental results compare the predictions made by our algorithm with human perceptual evaluations for several different parameter settings in our algorithm. The most popular existing algorithms are also evaluated. For some specific parameter settings, we find our algorithm provides better predictions, which are more closely matched to human perceptual evaluations, than the existing algorithms. The evaluations focus on the night vision application, but the algorithm we propose is applicable to other applications also.

Efficient JPEG 2000 VBR Compression with True Constant Perceived Quality

This paper describes a novel JPEG 2000 variable bit rate (VBR) encoding system for digital cinema that uses visually lossless viewing distance as the image quality metric to provide true constant perceived quality. In this approach, the wavelet coefficients are quantized using a human visual system model so that there are no detectable compression errors when a movie is viewed at the specified viewing distance (or at any greater distance). The compressed codestream includes only the minimum information that is needed to ensure visually lossless quality, so coding efficiency is high. Compression times are also significantly reduced, as compared to typical VBR systems, which results in a more efficient workflow. These benefits are achieved while maintaining full compatibility with the Digital Cinema Initiatives (DCI) compression specifications.

Perceptually adaptive joint deringing–deblocking filtering for scalable video transmission over wireless networks

Video transmission over low bit-rate channels, such as wireless networks, requires dedicated filtering during decoding for crucial enhancement of the perceptual video quality. For that reason, deringing and deblocking are inevitable components of decoders in wireless video transmission systems. Aimed at improving the visual quality of decoded video, in this paper a new perceptually adaptive joint deringing–deblocking filtering technique for scalable video streams is introduced. The proposed approach is designed to deal with artefacts inherent to transmissions over very low bit-rate channels, specifically wireless networks. It considers both prediction and update steps in motion compensated temporal filtering in an in-loop filtering architecture. The proposed approach integrates three different filtering modules to deal with low-pass, high-pass and after-update frames, respectively. The filter strength is adaptively tuned according to the number of discarded bit-planes, which in turn depends on the channel bit-rate and the channel error conditions. Furthermore, since ringing and blocking artefacts are visually annoying, relevant characteristics of the human visual system are considered in the used bilateral filtering model. That is, the amount of filtering is adjusted to the perceptual distortion by integrating a human visual system model into filtering based on luminance, activity and temporal masking. As a consequence, the resulting filter strength is automatically adapted to both perceptual sensitivity and channel variation. To assess the performance of the proposed approach, a comprehensive comparative evaluation against the conventional loop architecture and bilateral filter was conducted. The results of the experimental evaluation show a superior performance of the proposed adaptive filtering approach, providing better objective and subjective quality.

Design of Tone-Dependent Color-Error Diffusion Halftoning Systems

Grayscale error diffusion introduces nonlinear distortion (directional artifacts and false textures), linear distortion (sharpening), and additive noise. Tone-dependent error diffusion (TDED) reduces these artifacts by controlling the diffusion of quantization errors based on the input graylevel. We present an extension of TDED to color. In color-error diffusion, which color to render becomes a major concern in addition to finding optimal dot patterns. We propose a visually meaningful scheme to train input-level (or tone-) dependent color-error filters. Our design approach employs a Neugebauer printer model and a color human visual system model that takes into account spatial considerations in color reproduction. The resulting halftones overcome several traditional error-diffusion artifacts and achieve significantly greater accuracy in color rendition.

A robust image watermarking technique based on quantization noise visibility thresholds

A tremendous amount of digital multimedia data is broadcasted daily over the internet. Since digital data can be very quickly and easily duplicated, intellectual property right protection techniques have become important and first appeared about fifty years ago (see [I.J. Cox, M.L. Miller, The First 50 Years of Electronic Watermarking, EURASIP J. Appl. Signal Process. 2 (2002) 126–132. [52]] for an extended review). Digital watermarking was born. Since its inception, many watermarking techniques have appeared, in all possible transformed spaces. However, an important lack in watermarking literature concerns the human visual system models. Several human visual system (HVS) model based watermarking techniques were designed in the late 1990's. Due to the weak robustness results, especially concerning geometrical distortions, the interest in such studies has reduced. In this paper, we intend to take advantage of recent advances in HVS models and watermarking techniques to revisit this issue. We will demonstrate that it is possible to resist too many attacks, including geometrical distortions, in HVS based watermarking algorithms. The perceptual model used here takes into account advanced features of the HVS identified from psychophysics experiments conducted in our laboratory. This model has been successfully applied in quality assessment and image coding schemes M. Carnec, P. Le Callet, D. Barba, An image quality assessment method based on perception of structural information, IEEE Internat. Conf. Image Process. 3 (2003) 185–188, N. Bekkat, A. Saadane, D. Barba, Masking effects in the quality assessment of coded images, in: SPIE Human Vision and Electronic Imaging V, 3959 (2000) 211–219. In this paper the human visual system model is used to create a perceptual mask in order to optimize the watermark strength. The optimal watermark obtained satisfies both invisibility and robustness requirements. Contrary to most watermarking schemes using advanced perceptual masks, in order to best thwart the de-synchronization problem induced by geometrical distortions, we propose here a Fourier domain embedding and detection technique optimizing the amplitude of the watermark. Finally, the robustness of the scheme obtained is assessed against all attacks provided by the Stirmark benchmark. This work proposes a new digital rights management technique using an advanced human visual system model that is able to resist various kind of attacks including many geometrical distortions.

Using a Human Visual System Model to Optimize Soft-Copy Mammography Display: Influence of Veiling Glare

This project evaluated human observer performance and that of a human visual system model (JNDmetrix) to assess whether the veiling glare of a digital display influences observer performance during soft-copy interpretation of mammographic images for the detection of masses. A set of 160 mammographic images, half containing a single mass, was processed to simulate four levels of veiling glare: none, comparable to a medical grade monochrome curved-screen cathode ray tube (CRT) display, double that of the CRT and quadruple that of the CRT. The images were shown to six observers in a randomized presentation order on a liquid crystal display (LCD) that had essentially no veiling glare. The images were also analyzed using the JNDmetrix human visual system model. Observer performance as measured using receiver operating characteristic techniques declined with increasing veiling glare (F = 6.884, P = .0035), with quadruple veiling glare yielding significantly lower performance than the lower veiling glare levels. The JNDmetrix model did not show a large reduction in performance as a function of veiling glare, and correlation with the human observer data was modest (0.588). Soft-copy display veiling glare can influence observer performance, but only at extreme levels. The impact of veiling glare on performance may be more pronounced for less experienced readers.

Lapped-orthogonal-transform-based adaptive image watermarking

A robust, invisible watermarking scheme is proposed for digital images, where the watermark is embedded using the block-based lapped orthogonal transform (LOT). The embedding process follows a spread spectrum watermarking approach. In contrast to the use of transforms such as discrete cosine transform, our LOT watermarking scheme allows larger watermark embedding energy while maintaining the same level of subjective invisibility. In particular, the use of LOT reduces block artifacts caused by the insertion of the watermark in a block-by-block manner, hence obtaining a better balance between invisibility and robustness. Moreover, we use a human visual system (HVS) model to adaptively adjust the energy of the watermark during embedding. In our HVS model, each block is categorized into one of four classes (texture, fine-texture, edge, and plain-area) by using a feature known as the texture masking energy. Blocks with edges are also classified according to the edge direction. The block classification is used to adjust the watermark embedding parameters for each block.

A multi-factors approach for image quality assessment based on a human visual system model

In this paper, a multi-factor full-reference image quality index is presented. The proposed visual quality metric is based on an effective Human Visual System model. Images are pre-processed in order to take into account luminance masking and contrast sensitivity effects. The proposed metric relies on the computation of three distortion factors: blockiness, edge errors and visual impairments, which take into account the typical artifacts introduced by several classes of coders. A pooling algorithm is used in order to obtain a single distortion index. Results show the effectiveness of the proposed approach and its consistency with subjective evaluations.

On-Axis and Off-Axis Viewing of Images on CRT Displays and LCDs: Observer Performance and Vision Model Predictions 1

Although cathode-ray tube (CRT) displays typically are used for softcopy display of radiographs in the digital reading environment, liquid crystal displays (LCDs) currently are being used as an alternative. LCDs have many desirable viewing properties compared with a CRT, but significant image degradation can occur with off-axis viewing. This study compares observer performance and predictions from a human visual system model for on-axis and off-axis viewing for CRT displays versus LCDs. A set of mammograms with different lesion contrasts (n = 400) were shown to six radiologists on CRT and LCD monitors, once on-axis and once off-axis. Observer performance was measured by using receiver operating characteristic techniques. Performance was correlated with results of a human vision model designed to predict observer performance (just noticeable difference [JND]metrix model). Two approaches were used to generate model metrics; a paired discrimination and channelized model observer approach. The performance of human observers indicated that on-axis viewing with the LCD was better than with the CRT, but off-axis viewing was significantly better with the CRT than LCD (F = 8.8175; P < .0001). The paired discrimination model correctly predicted on-axis, but not off-axis, results. The channelized model observer correctly predicted both on- and off-axis results. Off-axis viewing of radiographic images on an LCD monitor degrades human observer performance significantly compared with a CRT display. Care should be taken in the clinic to avoid off-axis viewing during diagnostic interpretation.

A new image quality assessment based on HVS

This letter proposes a new kind of image quality philosophy—Modulate Quality based on Fixation Points (MQFP) based on Human Visual System (HVS) model. Dissimilar to the former HVS-based quality assessment, the new measure emphasizes particularly on modeling the jumping phenomenon of human sight instead of modeling the visual perception of human. In other words, to model the HVS using fixation points and stay-frequency instead of Contrast Sensitive Function (CSF) etc. which models the visual perception of HVS. The experiment on various frequency-distortion images indicates that the new measure is correlated with the subjective judgment more than the former HVS-based measure and is a robust measure.

A deblocking method for BDCT compressed images based on adaptive projections

A deblocking method based on projection onto convex sets (POCS) is proposed to reduce blocking artifacts in compressed images coded by the block discrete cosine transform. The method differs from existing POCS-based methods in three aspects. Firstly, the adjustment of a pixel's intensity is determined by local properties of the pixel. Secondly, three locally adaptive constraint sets are introduced to improve deblocking results. Thirdly, the human visual system modeling and relaxed projections are incorporated to make pixels adjust appropriately. The method is tested on typical images with excellent results.

Performance evaluation of an incorporated DCT block-based watermarking algorithm with human visual system model

In this paper we propose a new approach to predict a fixed fidelity level of a watermarked image while increasing the detection rate. The new approach incorporates a DCT block-based algorithm with human visual system (HVS). The performance of the new approach has been defined in terms of its effectiveness (detection rate) and fidelity. We show experimentally how our novel approach can be used to control distribution of a watermark to get a higher detection rate as well as better fidelity.

JPEG2000 compression coding using human visual system model

In order to apply the Human Visual System (HVS) model to JPEG2000 standard, several implementation alternatives are discussed and a new scheme of visual optimization is introduced with modifying the slope of rate-distortion. The novelty is that the method of visual weighting is not lifting the coefficients in wavelet domain, but is complemented by code stream organization. It remains all the features of Embedded Block Coding with Optimized Truncation (EBCOT) such as resolution progressive, good robust for error bit spread and compatibility of lossless compression. Well performed than other methods, it keeps the shortest standard code-stream and decompression time and owns the ability of VIsual Progressive (VIP) coding.

HVS-based medical image compression

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.

Color image denoising with wavelet thresholding based on human visual system model

Recent research in transform-based image denoising has focused on the wavelet transform due to its superior performance over other transform. Performance is often measured solely in terms of PSNR and denoising algorithms are optimized for this quantitative metric. The performance in terms of subjective quality is typically not evaluated. Moreover, human visual system (HVS) is often not incorporated into denoising algorithm. This paper presents a new approach to color image denoising taking into consideration HVS model. The denoising process takes place in the wavelet transform domain. A Contrast Sensitivity Function (CSF) implementation is employed in the subband of wavelet domain based on an invariant single factor weighting and noise masking is adopted in succession. Significant improvement is reported in the experimental results in terms of perceptual error metrics and visual effect.

Use of a Human Visual System Model to Predict Observer Performance with CRT vs LCD Display of Images

This Project evaluated a human visual system model (JNDmetrix) based on just noticeable difference (JND) and frequency-channel vision-modeling principles to assess whether a Cathode ray tube (CRT) or a liquid crystal display (LCD) monochrome display monitor would yield better observer performance in radiographic interpretation. Key physical characteristics, such as veiling glare and modulation transfer function (MTF) of the CRT and LCD were measured. Regions of interest from mammographic images with masses of different contrast levels were shown once on each display to six radiologists using a counterbalanced presentation order. The images were analyzed using the JNDmetrix model. Performance as measured by receiver operating characteristic (ROC) analysis was significantly better overall on the LCD display (P = 0.0120). The JNDmetrix model predicted the result (P = 0.0046) and correlation between human and computer observers was high (r (2) (quadratic) = 0.997). The results suggest that observer performance with LCD displays is superior to CRT viewing, at least for on-axis viewing.

Toward Robust Logo Watermarking Using Multiresolution Image Fusion Principles

This paper presents a novel robust watermarking approach called FuseMark based on the principles of image fusion for copy protection or robust tagging applications. We consider the problem of logo watermarking in still images and employ multiresolution data fusion principles for watermark embedding and extraction. A human visual system model based on contrast sensitivity is incorporated to hide a higher energy hidden logo in salient image components. Watermark extraction involves both characterization of attacks and logo estimation using a rake-like receiver. Statistical analysis demonstrates how our extraction approach can be used for watermark detection applications to decrease the problem of false negative detection without increasing the false positive detection rate. Simulation results verify theoretical observations and demonstrate the practical performance of FuseMark.