While objective and subjective quality assessment of 2D images and video have been an active research topic in the recent years, emerging 3D technologies require new quality metrics and methodologies taking into account the fundamental differences in the human visual perception and typical distortions of stereoscopic content. Therefore, this paper presents a comprehensive stereoscopic video database that contains a large variety of scenes captured using a stereoscopic camera setup consisting of two HD camcorders with different capture parameters. In addition to the video, the database also provides subjective quality scores obtained using a tailored single stimulus continuous quality scale (SSCQS) method. The resulting mean opinion scores can be used to evaluate the performance of visual quality metrics as well as for the comparison and for the design of new metrics.

In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks.

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Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.

In this paper a procedure for subjective evaluation of the new JPEG XR codec for compression of still pictures is described in details. The new algorithm has been compared to the existing JPEG and JPEG 2000 standards when considering compression of high resolution 24 bpp pictures, by mean of a campaign of subjective quality assessment tests which followed the guidelines defined by the AIC JPEG ah-hoc group. Sixteen subjects took part in experiments at EPFL and each subject participated in four test sessions, scoring a total of 208 test stimuli. A detailed procedure for statistical analysis of subjective data is also proposed and performed. The obtained results show high consistency and allow an accurate comparison of codec performance.