In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

In this paper, we explore the role that image composition plays in image aesthetic appeal classification. We propose low-level image composition features that approximate traditional photography composition guidelines, such as simplicity and visual balance (e.g. golden mean, golden triangles). We then use these features to build an image aesthetics classifier that we test with a baseline dataset. Interestingly, our approach, that only takes into account image composition features, yields close to state-of-the-art image aesthetic-based classification accuracies, which highlights the importance of image composition in image aesthetic appeal assessment.

Information overload is one of today's major concerns. As high-resolution digital cameras become increasingly pervasive, unprecedented amounts of social media are being uploaded to online social networks on a daily basis. In order to support users on selecting the best photos to create an online photo album, attention has been devoted to the development of automatic approaches for photo storytelling. In this paper, we present a novel photo collection summarization system that learns some of the users' social context by analyzing their online photo albums, and includes storytelling principles and face and image aesthetic ranking in order to assist users in creating new photo albums to be shared online. In an in-depth user study conducted with 12 subjects, the proposed system was validated as a first step in the photo album creation process, helping users reduce workload to accomplish such a task. Our findings suggest that a human audio/video professional with cinematographic skills does not perform better than our proposed system.

We demonstrate a system for automatic image selection for photobook creation, along with an intuitive user interface for fine tuning of the selection results. A versatile image collection representation is introduced, which allows for automatic scalable selection in order to target a specific image count for a predetermined size photobook. The images are selected based on their relevance, while preserving a good coverage of the event (time plus people) in order to maintain the storytelling potential of the selection. The selected images are laid out and presented to the user through an Adobe Flex user interface, which allows them to select images and swap them by semantically related ones, in an intuitive manner. The final result is output to a PDF file.

Image appeal may be defined as the interest that a photograph generates when viewed by human observers, incorporating subjective factors on top of the traditional objective quality measures. User studies were conducted in order to identify the right features to use in an image appeal measure; these studies also revealed that a photograph may be appealing even if only a region/area of the photograph is actually appealing. Extensive experimentation helped identify a good set of low level features, which were combined into a set of image appeal metrics. The appealing region detection and the image appeal metrics were validated with experiments conducted on 2000 ground truth images, each graded by three human observers. Two main image appeal metrics are presented: one that ranks the images based on image appeal with a high correlation with the human observations; and a second one which performs better at retrieving highly appealing images from an image collection.

This paper presents a contribution to image indexing applied to the document creation task. The presented method ranks a set of photographs based on how well they aesthetically work within a predefined document. Color harmony, document visual balance and image quality are taken into consideration. A user study conducted on people with a range of expertise in document creation helped gather the right visual features to consider by the algorithm. This shows some benefits for the traditional document creation task, as well as for the case of ever-changing web page banner colors and layout.