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The affective content of a movie is often considered to be largely determined by its style and aesthetics. Recently, studies have attempted to estimate affective movie content with computational features, but results have been mixed, one of the main reasons being a lack of data on perceptual stylistic and aesthetic attributes of film, which would provide a ground truth for the features. The distinctions between energetic and tense arousal as well as perceived and felt affect are also often neglected. In this study, we present a data set of ratings by 73 viewers of 83 stylistic, aesthetic, and affective attributes for a selection of movie clips containing complete scenes taken from mainstream movies. The affective attributes include the temporal progression of perceived and felt valence and arousal within the clips. The data set is aimed to be used to train algorithms that predict viewer assessments based on low-level computational features. With this data set, we performed a baseline study modeling the relation between a large selection of low-level computational features (i.e., visual, auditory, and temporal) and perceptual stylistic, aesthetic, and affective attributes of movie clips. Two algorithms were compared in a realistic prediction scenario: linear regression and the neural-network-based Extreme Learning Machine (ELM). Felt and perceived affect as well as stylistic attributes were shown to be equally easy to predict, whereas the prediction of aesthetic attributes failed. The performance of the ELM predictor was overall found to be slightly better than the linear regression. A feature selection experiment illustrated that features from all low-level computational modalities, visual, auditory and temporal, contribute to the prediction of the affect assessments. We have made our assessment data and extracted computational features publicly available.

In recent years, studies have estimated affective movie content computationally with stylistic features, yet knowledge of the perceptual relation between style and affect remains scarce. Such knowledge would be useful in affect-based movie recommendation systems. To this end, a user study was conducted in which seventy-three participants with varying levels of film expertise rated movie clips according to 13 stylistic features in three modalities (visual, aural and temporal) as well as perceived and felt affect in three dimensions (hedonic tone, energetic arousal and tense arousal). Style-based linear regression models were then constructed for each affect dimension. Visual features contributed the most to hedonic tone and tense arousal, and temporal features to energetic arousal. Also, perceived affect showed greater inter-rater agreement and better modeling performance than felt affect. The results indicate that the influence of specific stylistic features on affect varies by dimension and by whether the affect is perceived or felt.

Image quality is a vital criterion that guides the technical development of digital cameras. Traditionally, the image quality of digital cameras has been measured using test-targets and/or subjective tests. Subjective tests should be performed using natural images. It is difficult to establish the relationship between the results of artificial test targets and subjective data, however, because of the different test image types. We propose a framework for objective image quality metrics applied to natural images captured by digital cameras. The framework uses reference images captured by a high-quality reference camera to find image areas with appropriate structural energy for the quality attribute. In this study, the framework was set to measure sharpness. Based on the results, the mean performance for predicting subjective sharpness was clearly higher than that of the state-of-the-art algorithm and test-target sharpness metrics.

In this study the perception of crowds was investigated in urban environment. The images of crowds were viewed non-stereoscopically and stereoscopically with HMD (head-mounted display). The task of the participants was to count the number of persons in the crowds. The results clearly indicate that stereoscopic viewing enhances perception of crowds. The counting task was determined to be easiest with stereoscopic viewing, its error rate was significantly smaller and it was significantly preferred to non-stereoscopic viewing. The viewing method did not differ statistically with respect to the completion time.

The goal of the study was to develop a method for quality computation of digitally printed images. We wanted to use only the attributes which have a meaning for subjective visual quality experience of digitally printed images. Based on the subjective data and our assessments the attributes for quality calculation were sharpness, graininess and color contrast. The proposed graininess metric divides the fine detail image into blocks and used the low energy blocks for graininess calculation. The proposed color contrast metric computes the contrast of dominant colors using the coarse scale image. The proposed sharpness metric divides the coarse scale image into blocks and uses the high energy blocks for sharpness calculation. The reduced reference features of sharpness and graininess metrics are the numbers of high or low energy blocks. The reduced reference features of the color contrast metric are the directions of the dominant colors in reference image. The overall image quality was calculated by combining the values. The performance of proposed application specific image quality metric was high compared to the state of the art reduced reference applicationindependent image quality metric. Linear correlation coefficients between subjective and predicted MOS were 0.88 for electrophotography and 0.98 for ink-jet printed samples, for a sample set of 21 prints for electrophotography and for inkjet, subjectively evaluated by 28 observers.

The literature of visual quality is mainly concentrated on devising new physical, visual, or computational quality features which could indirectly reflect ldquotrue visual qualityrdquo. The problem is that the true visual quality is always a subjective and context sensitive judgement of a single individual or a group of individuals. Therefore, the developed methods are only loosely connected to this ultimate objective, and the existing de facto and official standards have been designed by forming a consensus among experts of a specific field (e.g., in the printing industry). In this study, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Then we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation trial. Finally by using the above framework, we devise the best combinations from a set of well-known measurable quantities. The devised combinations can be considered as optimal when agreement with the human visual quality experience is desired, and therefore, they also reveal completely novel information about measuring visual quality.

One of the most important research goals in media science is a computational model for the human perception of visual quality, that is, how to predict the subjective visual quality experience. This research area has converged to developing new and investigating existing lower-level measurable quantities, physical, visual or computational, which could explain the high level experience. A principal research question, whether the prediction of the visual quality experience based on any lower-level objective measurements is possible at all, has received much less attention. This question is investigated in this study. First, we describe a large psychological experiment where true factors of the human quality experience are pair-wise resolved for dedicatedly selected samples. Second, we describe a ranking measure which reveals the relationship between selected measurable quantities and the human evaluation. Finally, the presented ranking method is used to provide quantitative evidence that visual quality experience can be predicted using lower-level measurable quantities. This result is novel and by simultaneously revealing the underlying lower-level factors it should re-direct the future research towards the true model.

The psychological complexity of multivariate image quality evaluation makes it difficult to develop general image quality metrics. Quality evaluation includes several mental processes and ignoring these processes and the use of a few test images can lead to biased results. By using a qualitative/quantitative (Interpretation Based Quality, IBQ) methodology, we examined the process of pair-wise comparison in a setting, where the quality of the images printed by laser printer on different paper grades was evaluated. Test image consisted of a picture of a table covered with several objects. Three other images were also used, photographs of a woman, cityscape and countryside. In addition to the pair-wise comparisons, observers (N=10) were interviewed about the subjective quality attributes they used in making their quality decisions. An examination of the individual pair-wise comparisons revealed serious inconsistencies in observers' evaluations on the test image content, but not on other contexts. The qualitative analysis showed that this inconsistency was due to the observers' focus of attention. The lack of easily recognizable context in the test image may have contributed to this inconsistency. To obtain reliable knowledge of the effect of image context or attention on subjective image quality, a qualitative methodology is needed.

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

Full reference image quality algorithms are standard tools in digital image processing but have not been utilized for printed images due to a "correspondence gap" between the digital domain (a reference) and physical domain (printed sample). In this work, the authors propose a framework for applying full reference image quality algorithms to printed images. The framework consists of accurate scanning of printed samples and automatic registration and descreening procedures which bring the scans in correspondence with their digital originals. The authors complete the framework by incorporating state-of-the-art full reference algorithms to it. Using data from comprehensive psychometrical experiments of subjective quality experience, the authors benchmark the state-of-the-art methods and point out similar results in the digital domain: the best digital full reference measures, such as the recently introduced visual information fidelity algorithm, perform best also for printed media.