Light fields captured by the Lytro-Illum camera are the first to appear in the consumer market, capable of providing refocused pictures at acceptable spatial resolution and quality. Since this is partially due to sampling of a huge number of light rays, efficient compression methods are required to store and exchange light field data. This paper presents a performance study of HEVC-compatible coding of Lytro-Illum light fields using different data formats for standard coding. The efficiency of 5 different light field data formats are evaluated using a data set of 12 light field images and the standard HEVC coding configurations of Still-Image Profile, All-Intra, Low Delay B and P and Random Access. Unexpectedly, the results show that relative performance is not consistent across all coding configurations, raising new research questions regarding standard coding of Lytro-Illum light fields using HEVC. Most importantly, the proposed data formats greatly increase HEVC performance.

A new experimental technique for measuring the mobilities of positive ions in their parent gases is presented. The technique was applied to the rare gases, Ar, Kr, and Xe, and, for pressures typically below 10 Torr, two different types of positive ions were observed. The reduced mobilities of these ions in their parent gases were measured as a function of $E/N$ , the ratio of the electric field strength to the gas number density, at a temperature of $300\pm 1\text{K}$ . The results were compared with others available in the literature and the two ions were identified as being the atomic and the dimer rare gas ions. The results are in good agreement with those from other authors. Space charge and impurities effects are discussed.

Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. This paper presents a contribution for the research community, in the form of a publicly available light field image dataset of skin lesions (named SKINL2 v1.0). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further characterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.