Category search is a searching activity where the user has an example image and searches for other images of the same category. This activity often requires appropriate keywords of target categories making it difficult to search images without prior knowledge of appropriate keywords. Text annotations attached to images are a valuable resource for helping users to find appropriate keywords for the target categories. We propose an image exploration system in this article for category image search without the prior knowledge of category keywords. Our system integrates content-based and keyword-based image exploration and seamlessly switches exploration types according to user interests. The system enables users to learn target categories both in image and keyword representation through exploration activities. Our user study demonstrated the effectiveness of image exploration using our system, especially for the search of images with unfamiliar category compared to the single-modality image search.

Recently the movie industry has been advocating the use of frame rates significantly higher than the traditional 24 frames per second. This higher frame rate theoretically improves the quality of motion portrayed in movies, and helps avoid motion blur, judder and other undesirable artifacts. Previously we reported that young adult audiences showed a clear preference for higher frame rates, particularly when contrasting 24 fps with 48 or 60 fps. We found little impact of shutter angle (frame exposure time) on viewers’ choices. In the current study we replicated this experiment with an audience composed of imaging professionals who work in the film and display industry who assess image quality as an aspect of their everyday occupation. These viewers were also on average older and thus could be expected to have attachments to the “film look” both through experience and training. We used stereoscopic 3D content, filmed and projected at multiple frame rates (24, 48 and 60 fps), with shutter angles ranging from 90∘ to 358∘, to evaluate viewer preferences. In paired-comparison experiments we assessed preferences along a set of five attributes (realism, motion smoothness, blur/clarity, quality of depth and overall preference). As with the young adults in the earlier study, the expert viewers showed a clear preference for higher frame rates, particularly when contrasting 24 fps with 48 or 60 fps. We found little impact of shutter angle on viewers’ choices, with the exception of one clip at 48 fps where there was a preference for larger shutter angle. However, this preference was found for the most dynamic “warrior” clip in the experts but in the slower moving “picnic” clip for the naïve viewers. These data confirm the advantages afforded by high-frame rate capture and presentation in a cinema context in both naïve audiences and experienced film professionals.

Error diffusion is an often used method that transforms a continuous tone (multibit) image into an image of lower bit depth, most commonly into a binary output of black and white. The simplicity of the processing and the quality of the output have made error diffusion a frequently used tool. Part of the image quality is attributed to the minimization of quantization errors in the error-diffusion process. This article describes local instabilities in a color multilevel error-diffusion system that—in contrast—can lead to large local errors in the output, far exceeding the normally expected quantization errors. This can have serious negative effects specifically in connection with the design and incorporation of color calibration sheets.

The determination of local components in human skin from in vivo spectral reflectance measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. Hyperspectral imaging is a convenient technique since one spectrum is acquired in each pixel of the image, and by inverting a light scattering model, we can retrieve the concentrations of skin components in each pixel. The good performance of the method presented in this article comes from both the imaging system and the model. The hyperspectral camera that we conceived uses polarizing filters in order to remove gloss effects generated by the stratum corneum; it provides a high-resolution image (1120 × 900 pixels), with a thin spectral sampling of 10 nm over the visible spectrum. The acquisition time of 2 seconds is short enough to prevent movement effects of the imaged area, which is usually the main issue in hyperspectral imaging. The model relies on a two-layer model for the skin, and the Kubelka–Munk theory with Saunderson correction for the light reflection. An optimization method enables computing, in less than one hour, several skin parameters in each of the million of pixels. These parameters (blood, melanin and bilirubin volume fractions, oxygen saturation…) are then displayed under the form of density images. Different skin structures, such as veins, blood capillaries, hematoma or pigmented spots, can be highlighted. The deviation between the measured spectrum and the one computed from the fitted parameters is evaluated in each pixel.

This article proposes a new no-reference image quality assessment method that is able to blindly predict the quality of an image. The method is based on a machine learning technique that uses texture descriptors. In the proposed method, texture features are computed by decomposing images into texture information using multiscale local binary pattern (MLBP) operators. In particular, the parameters of local binary pattern operators are varied, which generates MLBP operators. The features used for training the prediction algorithm are the histograms of these MLBP channels. The results show that, when compared with other state-of-the-art no-reference methods, the proposed method is competitive in terms of prediction precision and computational complexity.

In this article, we propose a knowledge-based taxonomic scheme of the objective image quality assessment metrics including the key concepts involved for each approach. Our classification is constructed according to six criteria based on the information available at each stage of the design process. The novelty of the present classification scheme is that the six layers are linked via a single concept where each layer represents a single type of knowledge about: 1) the reference image, 2) the degradation type, 3) the visual perception field, 4) the human visual physiology and psychophysical mechanisms, 5) the processes of the visual information analysis, and finally 6) knowledge about perceptual image representation and coding. The first layer helps delineate boundaries between full-reference (FR) image quality assessment metrics, that are further classified through layers 2–6, and other families (reduced-reference [RR] and no-reference [NR]). In addition, gradual degrees are considered for knowledge about specific areas related to visual quality evaluation processes. The proposed taxonomic framework is intended to be stepwise, to help sorting out the fundamental ideas behind the development of objective image quality metrics often working on the luminance channel or marginally on the RGB channels. The aim is to congregate the already published classification schemes and to methodologically expand new aspects according to which an efficient and straightforward classification of the image quality assessment algorithms becomes possible. This is significant because of the increasing number of developed metrics. Furthermore, a systematic summarization is necessary in order to facilitate the research and application of image quality techniques.

Extracting hierarchical structures from networks provides us with an effective means of visualizing them, especially when they contain complicated node connectivities such as those in traffic and distributed networks. Although many techniques have been developed for such purposes, they often deterministically break unwanted cycles that may arise from inconsistencies in the network hierarchies, and thus never seek the best compromise among possible partial orders of nodes inherent in the cycle. This article presents an algorithm for inferring such partial orders by optimizing the network hierarchies along flow paths that are given as input. Our idea is to extract network hierarchies from round-trip paths as well as one-way ones by deriving reasonably consistent multi-layered structures even from possibly inconsistent flow data over the networks. This problem is formulated as mixed-integer programming where we incorporate additional constraints into fundamental layout criteria according to the type and/or expected use of the network. For better visual readability of the network layout, the nodes in individual layers are clustered and reordered for minimizing edge crossings, which is followed by fine adjustment of intervals between neighboring nodes. We study several network examples to demonstrate the feasibility of the proposed approach including course dependency charts, railway networks, and peer-to-peer (P2P) networks.

Storytelling animation has a great potential to be widely adopted by domain scientists for exploring trends in scientific simulations. However, due to the dynamic nature and generation methods of animations, serious concerns have been raised regarding their effectiveness for analytical tasks. This has led to interactive techniques often being favored over animations, as they provide the user with complete control over the visualization. This trend in scientific visualization design has not yet considered newer algorithmic animation generation methods that are driven by the automatic analysis of data features and storytelling techniques. In this work, the authors performed an experiment which compares feature-driven storytelling animations to common interactive visualization techniques for time-varying scientific simulations. They discuss the design of the experiment, including tasks for storm-surge analysis that are representative of common scientific visualization projects. Their results illustrate the relative advantages of both feature-driven storytelling animations and interactive visualizations, which may provide useful design guidelines for future storytelling and scientific visualization techniques.