This paper addresses the representation of binary images using mathematical morphology, a nonlinear theory for image processing, based on set theory. The new image representation, called "five-step" skeleton representation, is an extension of the morphological binary skeleton. It consists of calculating the morphological digital binary skeleton using squares or crosses and then reiterating the above procedure for skeleton subsets using lines (horizontal, vertical, 45° and 135°). The theoretical background of the new morphological iterative image representation is presented. Applications and results are illustrated by computer simulations.

This paper proposes a multiframe resolution enhancement algorithm by iteratively estimating and eliminating interpixel interference from neighboring pixels. The rationale is based on the observation that resolution reduction is caused by low-pass filtering nature of the image acquisition device as well as the digital zooming procedure. Both introduce interference between neighboring pixels. At a given location, the interpixel interference is from an integral effect of several low-pass filtering processes, each determined by one of its neighboring pixels. We propose using a Gaussian mixture to model the probability of the integral interpixel interference. The Gaussian mixture is determined by both the local image constraints and the local variation indicator (LVI). Local image constraints come from the image derivative priors, which evaluate the similarity between the current pixel and its neighborhood. A larger image derivative prior implies more interference. We use differences between the current pixel and its neighboring pixels as the image derivative priors. The LVI shows the reliability of the neighboring pixels. The LVI is obtained from both the temporal variation and the spatial variation. Neighboring pixels with larger LVIs are considered less reliable; hence, they provide less information for inferring the interpixel interference. Also, we show from the frequency domain representation that interpixel interference can actually be related to high-frequency loss. By estimating and compensating the missing high-frequency details iteratively, we can recover images with higher resolution. Experimental evaluation on varying inputs, including faces, synthetic text subjects, as well as license plates, validates the algorithm.

We address the representation of binary images using mathematical morphology. One of the main image representations in binary mathematical morphology is the shape decomposition representation, useful for image compression, pattern recognition, and image interpolation. The binary Morphological shape decomposition (MSD) representation can be developed and generalized. With these generalizations, the binary MSD's role as an efficient image decomposition tool is extended. Initially the MSD representation is based on only "one-parameter" families of elements. A new branch is added by introducing a multistructuring element MSD based on the decomposition of images into "multiparameter" families of elements. The MSD representation contains redundant points. Examples are presented and illustrated by computer simulations.

We propose techniques to analyze bilateral asymmetry in mammograms by combining directional information, morphological measures, and geometric moments related to density distributions. The procedure starts by detecting the breast boundary and the pectoral muscle edge (in mediolateral-oblique, or MLO, views). All artifacts outside the breast boundary as well as the pectoral muscle region are eliminated. A breast density model based upon a Gaussian mixture model is then used to segment the fibroglandular disks of the mammograms. Rose diagrams are used to map the magnitude and directional information related to the fibroglandular tissue filtered using multiresolution Gabor wavelets. The directional data of the left and right mammograms are aligned by using the straight lines perpendicular to the corresponding pectoral muscle edges and subtracted to obtain difference rose diagrams. Directional features are obtained from the difference rose diagrams and used to characterize the changes caused by the development of breast cancer in the form of bilateral asymmetry or architectural distortion. An additional set of features including Hu's moments, eccentricity, stretch, area, and average density are extracted from the segmented fibroglandular disks. The differences between the pairs of the features for the left and right mammograms are used as measures for the analysis of asymmetry. The techniques were applied to 88 mammograms from the Mini-MIAS database. Classification accuracies of up to 84.4% were achieved, with sensitivity and specificity rates of 82.6% and 86.4%, respectively.

A number of successful tone mapping operators for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range (LDR) devices. They were inspired by fields as diverse as image processing, photographic practice, and modeling of the human visual systems (HVS). The variety of approaches calls for a systematic perceptual evaluation of their performance. We conduct a psychophysical experiment based on a direct comparison between the appearance of real-world scenes and HDR images of these scenes displayed on an LDR monitor. In our experiment, HDR images are tone mapped by seven existing tone mapping operators. The primary interest of this psychophysical experiment is to assess the differences in how tone mapped images are perceived by human observers and to find out which attributes of image appearance account for these differences when tone mapped images are compared directly with their corresponding real-world scenes rather than with each other. The human subjects rate image naturalness, overall contrast, overall brightness, and detail reproduction in dark and bright image regions with respect to the corresponding real-world scene. The results indicate substantial differences in the perception of images produced by individual tone mapping operators. We observe a clear distinction between global and local operators—in favor of the latter—and we classify the tone mapping operators according to naturalness and appearance attributes.

The development of metrics for assessing the quality of compressed images has given rise to considerable effort and has led to a general layout of the perceptual metrics based on models of the human visual system. Instead of another comparative study of the global performance of these systems, we sought to perform an individual evaluation of the main visual model components in order to propose a unified fidelity metric with low complexity and high performance. Our effort has focused on the frequency selectivity of human vision, perceived contrast, masking effects, pooling, and visual attention. We used the correlation between the obtained fidelity measures and the subjective assessment scores to analyze the different configurations.

Gaze coordinates are recorded from naive subjects watching a number of video clips. These are used to derive volumes of interest (VOIs) representing the mutual visual interest of the tested subjects. Video frames are then processed in variable resolution contingent on the VOI shapes to produce off-line foveated video. We report on bit rate savings between 30 and 54% due to off-line foveation proir to encoding with H.264. Subjective tests with a new set of subjects showed that off-line foveation in two of the three tested video clips had a positive effect on the experienced quality compared to unfoveated videos compressed with the same quality factor. We conclude that, in disagreement with previous works, it is advantageous to use off-line foveation to improve the performance of current state-of-art video compression technology.

We consider the robust packetization scheme for set partitioning in hierarchical trees (SPIHT) embedded bit stream over packet erasure channels and propose a fast and robust fixed-length packetization scheme using a two-step process. In the first step, a multiplexer is used to partition the embedded bit stream into multiple variable-length blocks with block sizes close to the fixed packet size. In the second step, a packetized error-resilient entropy code (PEREC) algorithm is proposed to reorganize these variable-length blocks into fixed-size packets to be transmitted over the physical network. Any lost packets have only minor effects on other received packets. Computer simulations show that the proposed method provides both excellent compression performance with fast speed and graceful degradation under serve packet losses.

With the proliferation of digital multimedia content, information hiding techniques have attracted more and more research interests. Information hiding not only hides the content and location of the protected data, but also hides its very existence. High-bitrate information hiding is different from digital watermarking in that it tries to hide relatively large amounts of auxiliary information instead of just one or a few verification bits. This research is mainly concerned with channel capacity and robustness to lossy image/video codec. In the proposed DCT (discrete cosine transformation) domain algorithm, 1 bit is hidden within each 8×8 DCT coefficient block by means of vector quantization. A mathematical model has been proposed to predict the bit error rate (BER) of the algorithm under transformation-based lossy compression. The channel capacity can be increased 4-fold with further performance improvement. Digital video information hiding is a natural extension of digital image information hiding. Experimental results show that the proposed algorithm is very robust to lossy image/video codec. It is also observed that hidden information within different types of frames/slices have different levels of robustness against H.264/AVC compression. The proposed algorithm shall be very useful in practical applications such as side information delivery, captioning, video-in-video, and speech-in-video.

We propose a fragile watermarking scheme in the wavelet transform domain that is sensitive to all kinds of manipulations and has the ability to localize the tampered regions. To achieve high transparency (i.e., low embedding distortion) while providing protection to all coefficients, the embedder involves all the coefficients within a hierarchical neighborhood of each sparsely selected watermarkable coefficient during the watermark embedding process. The way the nonwatermarkable coefficients are involved in the embedding process is content-dependent and nondeterministic, which allows the proposed scheme to put up resistance to the so-called vector quantization attack, Holliman-Memon attack, collage attack, and transplantation attack.

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it can eliminate directional hysteresis completely. However, due to its frame-oriented processing nature, the computational complexity is comparatively high. Furthermore, though theoretically MED can remove directional hysteresis by eliminating predefined scanning paths and causal filters, no comprehensive quantitative analysis on this issue can be found in the literature. A fast MED algorithm is proposed and a detailed analysis on the performance of various MED algorithms including the proposed one are provided. Analysis and simulation results show that the proposed algorithm can reduce the computational complexity without sacrificing the image quality as compared with conventional MED algorithms. The proposed algorithm also supports parallel processing and hence can further reduce the processing time.

In the visualization of three-dimensional (3D) images, specific isosurfaces are usually extracted from 3D images and used to represent (approximate) boundary surfaces of certain structures within 3D images. In order to well approximate the boundary surfaces of these structures, it is important to determine a good isosurface for each boundary surface. An isosurface is said to be a good isosurface of a boundary surface if it can approximate the boundary surface with the smallest error under certain error measuring criteria. The mathematical model describing the approximation problem of a boundary surface by isosurfaces is constructed and studied. The method used to deduce good isosurfaces for the boundary surfaces within 3D discrete images is presented. The proposed method is illustrated by examples with different real 3D biomedical images.

Window-based correlation algorithms are widely used for stereo matching due to their computational efficiency as compared to global algorithms. In this paper, a multiple window correlation algorithm for stereo matching is presented which addresses the problems associated with a fixed window size. The developed algorithm differs from the previous multiple window algorithms by introducing a reliability test to select the most reliable window among multiple windows of increasing sizes. This ensures that at least one window is large enough to cover a region of adequate intensity variations while at the same time small enough to cover a constant depth region. A recursive computation procedure is also used to allow a computationally efficient implementation of the algorithm. The outcome obtained from a standard set of images with known disparity maps shows that the generated disparity maps are more accurate as compared to two popular stereo matching local algorithms.

In this paper, stereo image reconstruction using regularized adaptive disparity estimation is proposed. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. Especially, in the proposed algorithm, the first feature values are extracted from the input stereo images pair. Then, a matching window for stereo matching is adaptively selected depending on the magnitude of these feature values. That is, for the region having larger feature values, a smaller matching window is selected, while, for the opposite case, a larger matching window is selected by comparing predetermined threshold values. This approach is not only able to reduce a mismatching of disparity vectors, which occurs in the conventional dense disparity estimation with a small matching window, but is also able to reduce blocking effects that occur in the coarse disparity estimation with a large matching window. In addition, in this paper, a new regularized adaptive disparity estimation technique is proposed. That is, by regularizing the estimated disparity vector with the neighboring disparity vectors, problems of the conventional adaptive disparity estimation scheme might be solved, and the predicted stereo image can be more effectively reconstructed. From experiments using stereo sequences of "Man", "Fichier", "Manege", and "Tunnel", it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 6.90 dB on average at ±30 search ranges as compared to those of conventional algorithms. Also, it is found that there is almost no difference between an original image and a reconstructed image through the proposed algorithm by comparison to that of conventional algorithms.

A new high-performance scalable systolic array processor architecture module for implementation of the two-dimensional discrete convolution algorithm on an (i×j) pixel input image plane (IP) using an (n×n) filter coefficient (FC) plane is first presented. The module generates one convoluted output image (OI) plane pixel per system clock cycle for an (n×n) FC plane using a level of r hardware resources. Second, the architecture is extended in a modular scalable manner to allow simultaneous convolution of a single IP, with k different (n×n) FC planes, such that k convoluted OI plane pixels are generated each system clock cycle, utilizing less than (k*r) hardware resources. The new convolution architecture may be implemented to an ASIC or programmable logic device (PLD) platform. Results of synthesizing and implementing the proposed architecture are shown, illustrating the scalability of the new convolution architecture relative to k. Results from postimplementation virtual hardware prototype simulation testing and from testing a PLD-based experimental hardware prototype are shown that validate correct functional and performance operation of the new convolution architecture module.

This paper proposes a new low-complexity, near-lossless image compression method and its application-specific integrated circuit (ASIC) design for low power and high frame rate in the wireless endoscopy capsule system. Assuring high image quality (PSNR larger than 46.37 dB and pixel error less than 2), the proposed method can provide low complexity, low storage overhead, as well as excellent compression performance compared with other conventional interpolation-first methods and other existing similar compression-first methods. The VLSI implementation functionally consists of two pipelined parts: the preprocessor and the JPEG-LS engine. A parallel, fully pipelined VLSI architecture with a clock management mechanism is proposed for the JPEG-LS engine, which ensures a low power application and real-time data processing. The hardware implement has been verified on FPGA and implemented in 0.18-µm CMOS technology.

This PDF file contains the errata for “JEI Vol. 16 Issue 01 Paper 2713758” for JEI Vol. 16 Issue 01

This PDF file contains the errata for “JEI Vol. 16 Issue 01 Paper 2713759” for JEI Vol. 16 Issue 01

This PDF file contains the editorial “Digital Heritage: Applying Digital Imaging to Cultural Heritage” for JEI Vol. 16 Issue 01