Die Anstalt zur Bereitung künstlicher Mineralwässer in Riga

http://www.ester.ee/record=b4010548*es

Lighting Quality Evaluations using Images on a High Dynamic Range Display

Limited research comparing participant ratings of luminous environments to ratings of images of those environments indicates that images can be a reasonable surrogate for the real space, particularly on ratings related to aesthetics. However, the realism of such images when presented on computer screens is potentially limited by conventional display technologies that cannot reproduce the full range of luminances in real spaces. In this pilot experiment we used a new, high dynamic range (HDR) computer monitor capable of producing screen luminances and contrasts comparable to those in a real space. Fifty-four participants viewed three images of a conventional office in two display modes: HDR monitor and conventional monitor. Participants rated each image for room appearance, environmental satisfaction and realism. These ratings were also compared to similar ratings made by participants in an earlier experiment (reported in 1998) who occupied the real spaces depicted in the images. Results indicate that computer screen images are perceived in a similar way as real luminous environments. HDR images are perceived differently than images on a conventional monitor: they are rated as brighter and less attractive, as expected. Given their more authentic luminances, HDR images should be perceived as more similar to the real space, but our results neither support nor refute this

BitNet: Learning-Based Bit-Depth Expansion

Bit-depth is the number of bits for each color channel of a pixel in an image. Although many modern displays support unprecedented higher bit-depth to show more realistic and natural colors with a high dynamic range, most media sources are still in bit-depth of 8 or lower. Since insufficient bit-depth may generate annoying false contours or lose detailed visual appearance, bit-depth expansion (BDE) from low bit-depth (LBD) images to high bit-depth (HBD) images becomes more and more important. In this paper, we adopt a learning-based approach for BDE and propose a novel CNN-based bit-depth expansion network (BitNet) that can effectively remove false contours and restore visual details at the same time. We have carefully designed our BitNet based on an encoder-decoder architecture with dilated convolutions and a novel multi-scale feature integration. We have performed various experiments with four different datasets including MIT-Adobe FiveK, Kodak, ESPL v2, and TESTIMAGES, and our proposed BitNet has achieved state-of-the-art performance in terms of PSNR and SSIM among other existing BDE methods and famous CNN-based image processing networks. Unlike previous methods that separately process each color channel, we treat all RGB channels at once and have greatly improved color restoration. In addition, our network has shown the fastest computational speed in near real-time.Comment: Accepted by ACCV 2018, Authors Byun and Shim contributed equall