Psychovisual Research Articles

An Adaptive Image Quality Assessment Algorithm

An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

EVALUATING A SEGMENTATION-RESISTANT CAPTCHA INSPIRED BY THE HUMAN VISUAL SYSTEM MODEL

Visual CAPTCHAs are widely used these days on the Internet as a means of distinguishing between humans and computers. They help protect servers from being flooded by requests from malicious scripts. However, they are not very secure. Numerous image processing algorithms are able to discern the characters used in the CAPTCHAs. It has been suggested that CAPTCHAs can be made more secure if they are distorted in ways that makes segmentation difficult. However, out of all the reviewed distortions present in current CAPTCHAs there are none that allow for a high level of segmentation difficulty. Furthermore, CAPTCHAs also need to be used by humans who may not find certain distortions tolerable. Thus, the problem of selecting a good distortion becomes a tradeoff between user acceptability and computer solvability. It is hypothesized in this paper that rather than use low-level image distortions, optical distortions based on the Gestalt laws of perception that govern human visual system models should be applied. These distortions would ensure widespread user acceptability (as they are based on the internal workings of the HVS), and be very difficult for computers to solve (as HVS perception models have been difficult to implement in computers). This paper aims to explore the feasibility of employing Gestalt-inspired distortion in CAPTCHAs by first implementing a CAPTCHA cracker and then evaluating the performance of some manually generated Gestalt CAPTCHA’s against some existing CAPTCHAs.

An Efficient Selective Perceptual-Based Super-Resolution Estimator

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.

Content-based Image Hiding Method for Secure Network Biometric Verification

For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content correlation analysis to protect the network-based transmitted image. By using principal component analysis (PCA), the content correlation between the biometric image and the cover image is firstly analyzed. Then based on particle swarm optimization (PSO) algorithm, some regions of the cover image are selected to represent the biometric image, in which the cover image can carry partial content of the biometric image. As a result of the correlation analysis, the unrepresented part of the biometric image is embedded into the cover image by using the discrete wavelet transform (DWT). Combined with human visual system (HVS) model, this approach makes the hiding result perceptually invisible. The extensive experime...

The use of the contrast sensitivity function in the perceptual quality assessment of fused image

Numerous algorithms have been proposed to fuse multi-sensor images for a diverse range of applications. The fused result is also presented as an image, which is more informative than any individual input. Accordingly, a number of fusion metrics are developed to assess the performance of the fusion operation or the quality of the fused image. Among these metrics, two approaches, which are based on human visual system models, employ the contrast sensitivity function (CSF) in its implementations. In CSF, viewing distance is a crucial parameter determining the filtering bandwidth. However, the effect of viewing distance was not discussed in the available literature. This letter is to clarify the use of CSF in the human perception-based fusion metrics. The experimental results illustrate how the viewing distance affects the fusion quality assessment and Matlab™ code is provided in this letter.

Processing real-world imagery with FACADE-based approaches

This paper considers the processing of real-world imagery in the so-called Form-And-Color-And-DEpth (FACADE) framework, which features some superior mechanisms of the human vision system (HVS). FACADE framework was originally proposed by Grossberg et al. as an integrative model of the HVS to illustrate the possible procedures for visual perception of shape (the boundary contour), surface (luminance and color), and binocular depth. As a simplified, reasonable and mathematically full-fledged approach to the HVS, we saw FACADE as a promising infrastructure through which to construct a powerful image processing engine. However, in our attempts to use the approach in its original modality, to deal with real-world imagery, we found it to be inefficient and non-robust.

Image Quality Assessment Based on Fuzzy Similarity Measure and Wavelet Transform

Based on the characteristics of wavelet coefficients of images and fuzzy similarity measure, a novel image quality assessment is proposed in this paper. Firstly, the reference image and the distorted images are decomposed into several levels by means of wavelet transform respectively. The approximation and detail coefficients of the reference image (the distorted images) are as the reference sequences (the comparative sequences). Secondly, select the right membership function to map the referenced sequences and the comparative sequences to a membership value between 0 and 1 respectively. And calculate the fuzzy similarity measure values between the reference sequences and the comparative sequences respectively. Moreover, image quality assessment matrix of every distorted image can be constructed based on the fuzzy similarity measure values and image quality can be assessed. The algorithm makes full use of perfect integral comparison mechanism of fuzzy similarity measure and the well matching of discrete wavelet transform with multi-channel model of human visual system. Experimental results show that the proposed algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system than the traditional method PSNR.

A Study of Digital Watermark Algorithm Based on HVS for Halftone Images

Special printing material and special techniques are used for the purpose of fighting against the piracy of the presswork. But these methods require high standard technology and high production costs. Using digital watermark can guarantee a strong and low cost protection for the presswork. Existing digital watermark algorithms for halftone images are mostly applied to FM (Frequency Modulation) screening and these algorithms usually complete the embedding process by modulating the coefficients of the frequency domain. Such algorithms are relatively complex, and they are easy to lose information so that it will make a lower detection probability. Moreover, in the actual printing process, AM (Amplitude Modulation) screening is the more mature and more widely used method compared with FM screening. The subject of this article is monochrome presswork generated by AM screening. The structure of this article is as follows: The first section uses the principle of HVS (Human Visual System) model to select the area in which the watermark will be embedded. The second section uses different screening threshold matrixes to create the printing dots in the selected area. In this way, a texture of certain direction is formed. The last section uses Fourier Power Spectrum method to detect the texture features of the screened image to complete the detection of the watermark. The result of the experiment shows that this algorithm can generate a digital watermark of good transparency and high detection effectiveness. It combines a unique AM screening approach to complete the embedding process. All the processes do not require any additional equipment, and the embedding and detection process are quite simple.

Perceived Image Gamut Based on Human Visual System and Wavelet Transform

As some images perhaps contain pixels whose individual colors will not be perceived if they are below the spatial threshold of human visual system (HVS), the paper presents a wavelet transform method for color image considering HVS characteristics to reduce the redundant color information and then construct perceived gamut of image (PGI), which will be used for effective image-dependent gamut mapping. Taking into account the Contrast Sensitivity Function (CFS) of HVS model, the color information reduction is implemented in some subbands of the wavelet transform domain. Then the image gamut was visualized using the general convex hull algorithm and the constructed PGI will only indicate the perceived part of color image. We preliminarily establish a method for reliably determining PGI, which is help to developing robust and psycho-visually justified image gamut mapping algorithm in the future.

Video quality assessment for computer graphics applications

Numerous current Computer Graphics methods produce video sequences as their outcome. The merit of these methods is often judged by assessing the quality of a set of results through lengthy user studies. We present a full-reference video quality metric geared specifically towards the requirements of Computer Graphics applications as a faster computational alternative to subjective evaluation. Our metric can compare a video pair with arbitrary dynamic ranges, and comprises a human visual system model for a wide range of luminance levels, that predicts distortion visibility through models of luminance adaptation, spatiotemporal contrast sensitivity and visual masking. We present applications of the proposed metric to quality prediction of HDR video compression and temporal tone mapping, comparison of different rendering approaches and qualities, and assessing the impact of variable frame rate to perceived quality.

Perceptually adaptive spread transform image watermarking scheme using Hadamard transform

The present paper proposes a digital image watermarking scheme using the characteristics of the human visual system (HVS), spread transform technique and statistical information measure. Spread transform (ST) scheme is implemented using the transform coefficients of both the host and the watermark signal. Watermark embedding strength is adaptively adjusted using frequency sensitivity, luminance, contrast and entropy masking of HVS model. The choice of Hadamard transform as watermark embedding domain offers several advantages, such as low loss in image information (higher image fidelity), greater reliability of watermark detection and higher data hiding capacity at high degree of compression. Performance of the proposed method is compared with a number of recently reported watermarking schemes based on spread spectrum (SS) and quantization index modulation (QIM).

Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method

extraction is an important component of a pattern recognition system. A well-defined feature extraction algorithm makes the identification process more effective and efficient. Quality checking is one of the most prominent steps in many applications using Feature extraction. Several techniques exist for the quality checking of wooden materials. However, image based quality checking of wooden materials still remains a challenging task. Although trivial quality checking methods are available, they do not give useful results in most situations. This paper addresses the issue of quality checking of wooden materials using feature extraction techniques with high accuracy and reliability. Experiments conducted under the proposed conditions showing significant results are presented. Quality checking has been a focus of investigation over last decades. Material quality checking plays a vital role in product based industries. Most of the literature concentrates on quality checking of material based on dynamic motion of image capturing. This paper presents a noval approach for quality checking of wooden material based on correlation Coefficient based feature extraction technique. Pasquale Flauto and Salvatore Musella (1) has proposed a technique and quality control methods used during the development phases of an expert system prototype, known as Hydronet for conventional (procedural)software systems used by most informatics leading companies. Junfeng Li and Wenzhan Dai al.(2) has proposed the algorithm that makes full use of perfect integral comparison mechanism of the correlation coefficient and the well matching of discrete wavelet transform with multichannel model of human visual system. The algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system then the traditional method PSNR. Christof Knoess et al.(3) has presented their work on daily quality checking of the natural background radioactivity from the new scintillator material LSO as a uniform source. Liwei Wang et al (4) has proposed that the matrices based 2-D algorithms are equivalent To special cases of image block based feature extraction. This method reduces the computational effort and the possibility of singularity in feature extraction Chulhee Lee and Euisun Choi (5) has proposed a method to optimize feature extraction for multiclass problems. The algorithm consistently provides better performances compared with the conventional feature extraction algorithms. Yong- zhi Li et al (6) has proposed the method with more powerful capability to eliminate the statistical correlation between features and improve efficiency of feature extraction. This is better method than original KMMC and kernel principal component analysis (KPCA) in terms of efficiency and stability about feature extraction. Andreas Hanemann and Martin Sailer .(7) has presented a framework for the new kind of event correlation which is called service-oriented event correlation. This bridges the gap between the management of the infrastructure and the offer of services for the customers with respect to the service fault diagnosis. The organization of the paper is as follows: section 2 describes statistical features extracted from image section 3 focuses on outline of the approach, section 4 deals on experiment and results, section 5 deals with discussion and section 6 concludes with conclusion.

67.4: Use of a Spatial Multiresolution Visual Model with Display Characterization and Ambient Information to Adaptively Shape the Tonescales of Mobile Displays

AbstractAn image‐dependent, display‐dependent, and ambient illumination‐dependent tone‐mapping operator is developed that is driven by a human vision system model. It aims to maximize visible contrast information for the full range of display devices, from e‐paper to HDR displays. The operator weights contrast distortions according to their visibility predicted by the model of the human visual system. The distortions are minimized given a display model that incorporates ambient information to enforce constraints on the solution. A scene cut algorithm is used for video content to allow rapid tonescale changes across scenes while a temporal LPF is used to avoid visible temporal fluctuations within scenes. We discuss the differences between our method and previous approaches to the tone‐mapping problem, as well as newer tone mapping approaches that do not seek to preserve image information. This paper will especially highlight the issues important for current display technology for mobile devices.

Using Human Visual System modeling for bio-inspired low level image processing

An efficient modeling of the processing occurring at retina level and in the V1 visual cortex has been proposed in [1,2]. The aim of the paper is to show the advantages of using such a modeling in order to develop efficient and fast bio-inspired modules for low level image processing. At the retina level, a spatio-temporal filtering ensures accurate structuring of video data (noise and illumination variation removal, static and dynamic contour enhancement). In the V1 cortex, a frequency and orientation based analysis is performed. The combined use of retina and V1 cortex modeling allows the development of low level image processing modules for contour enhancement, for moving contour extraction, for motion analysis and for motion event detection. Each module is described and its performances are evaluated. The retina model has been integrated into a real-time C/C++ optimized program which is also presented in this paper with the derived computer vision tools.

A novel image hiding approach based on correlation analysis for secure multimodal biometrics

This paper proposes a novel multimodal biometric images hiding approach based on correlation analysis, which is used to protect the security and integrity of transmitted multimodal biometric images for network-based identification. Compared with existing methods, the correlation between the biometric images and the cover image is first analyzed by partial least squares (PLS) and particle swarm optimization (PSO), aiming to make use of the abundant information of cover image to represent the biometric images. Representing the biometric images using the corresponding content of cover image results in the generation of the residual images with much less energy. Then, considering the human visual system (HVS) model, the residual images as the secret images are embedded into the cover image using middle-significant-bit (MSB) method. Extensive experimental results demonstrate that the proposed approach not only provides good imperceptibility but also resists some common attacks and assures the effectiveness of network-based multimodal biometrics identification.

A WAVELET-BASED PERCEPTUAL IMAGE CODER INCORPORATING A NEW MODEL FOR COMPRESSION OF COLOR IMAGES

A wavelet-based perceptual image coder for the compression of color images is proposed here in which the coding structure is coupled with Human Visual System models to produce high quality images. The major contribution is the development of a new model for the compression of the color components based on psychovisual experiments, which quantifies the optimum amount of compression that can be applied to the color components for a given rate. The model is developed for YCbCr color space and the perceptually uniform CIE Lab color space. A complete coding structure for the compression of color images is developed by incorporating the new perceptual model. The performance of the proposed coder is compared with a wavelet-based coder that uses the quantization stage of the JPEG2000 standard. The perceptual quality of the compressed images is tested using the wavelet-based subjective and objective perceptual quality matrices such as Mean Opinion Score, Visual Information Fidelity and Visual Signal to Noise Ratio. Though the model is developed for a perceptually lossless high quality image compression, results obtained reveal that the proposed structure gives very good perceptual quality compared to the existing schemes for lower bit rates. These advantages make the proposed coder a candidate for replacing the encoder stage of the current image compression standards.

Least-Squares Halftoning via Human Vision System and Markov Gradient Descent (LS-MGD): Algorithm and Analysis

Halftoning is the core algorithm governing most digital printing or imaging devices, by which images of continuous tones are converted to ensembles of discrete or quantum dots. It is through the human vision system (HVS) that such fields of quantum dots can be perceived to be almost identical to the original continuous images. In the current work, we propose a least-squares-based halftoning model with a substantial contribution from the HVS model, and we design a robust computational algorithm based on Markov random walks. Furthermore, we discuss and quantify the important role of spatial smoothing by the HVS and rigorously prove the gradient-descent property of the Markov-stochastic algorithm. Computational results on typical test images further confirm the performance of the new approach. The proposed algorithm and its mathematical analysis are generically applicable to similar discrete or nonlinear programming problems.

A visible watermarking algorithm based on the content and contrast aware (COCOA) technique

A novel visible watermarking algorithm based on the content and contrast aware (COCOA) technique with the consideration of Human Visual System (HVS) model is presented in this study. In order to determine the optimal watermark locations and strength at the watermark embedding stage, the COCOA visible watermarking utilizes the global and local characteristics of the host and watermark images in the discrete wavelet transform (DWT) domain. To achieve the best tradeoff between the embedding energy of watermark and the perceptual translucence, the utilization of contrast–sensitive function, noise visible function of perceptual model, and the basis function amplitudes of DWT coefficients are fine tuned, for the best quality of perceptual translucence and noise reduction of the COCOA algorithm. The experimental results demonstrate that COCOA technique not only provides high PSNR values for the watermarked images, but also preserves the watermark visibility under various signal processing operations, especially the watermark removal attack.

Predicting Display Visibility Under Dynamically Changing Lighting Conditions

AbstractDisplay devices, more than ever, are finding their ways into electronic consumer goods as a result of recent trends in providing more functionality and user interaction. Combined with the new developments in display technology towards higher reproducible luminance range, the mobility and variation in capability of display devices are constantly increasing. Consequently, in real life usage it is now very likely that the display emission to be distorted by spatially and temporally varying reflections, and the observer's visual system to be not adapted to the particular display that she is viewing at that moment. The actual perception of the display content cannot be fully understood by only considering steady‐state illumination and adaptation conditions. We propose an objective method for display visibility analysis formulating the problem as a full‐reference image quality assessment problem, where the display emission under “ideal” conditions is used as the reference for real‐life conditions. Our work includes a human visual system model that accounts for maladaptation and temporal recovery of sensitivity. As an example application we integrate our method to a global illumination simulator and analyze the visibility of a car interior display under realistic lighting conditions.

OPTIMAL RATE ALLOCATION FOR LOGO WATERMARKING

Compression of logo as an image is addressed in logo watermarking, in which a framework of joint Human Vision System (HVS) model and rate allocation theory in the wavelet domain is applied. Under this framework, a novel logo watermarking algorithm using reversible discrete wavelet transform is proposed. Based on multi-level wavelet decomposition of both host and logo images, a well-known HVS model is applied to locate the visually insensitive area in the host for embedding, while the rate allocation theory determines how the logo is compressed and embedded by using the statistical characteristics of the logo. For a given overall embedding distortion, quantization step-sizes of different logo subbands are analytically determined to maximize the fidelity of the extracted logo under the imperceptibility constraint. The adaptive system is thus applicable to different hosts and logos without tuning the parameters manually. It is proved to be robust against various types of attacks and is quite suitable for hardware implementations. Since logo is considered as an image in this new algorithm, the watermarking approach developed can be extended in general to image-in-image embedding.