Testing HDR image rendering algorithms

Eight high-dynamic-range image rendering algorithms were tested using ten high-dynamic-range pictorial images. A large-scale paired comparison psychophysical experiment was developed containing two sections, comparing the overall rendering performances and grayscale tone mapping performance respectively. An interval scale of preference was created to evaluate the rendering results. The results showed the consistency of tone-mapping performance with the overall rendering results, and illustrated that Durand and Dorsey’s bilateral fast filtering technique and Reinhard’s photographic tone reproduction have the best rendering performance overall. The goal of this experiment was to establish a sound testing and evaluation methodology based on psychophysical experiment results for future research on accuracy of rendering algorithms

CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering

Intrinsic image decomposition is a challenging, long-standing computer vision problem for which ground truth data is very difficult to acquire. We explore the use of synthetic data for training CNN-based intrinsic image decomposition models, then applying these learned models to real-world images. To that end, we present \ICG, a new, large-scale dataset of physically-based rendered images of scenes with full ground truth decompositions. The rendering process we use is carefully designed to yield high-quality, realistic images, which we find to be crucial for this problem domain. We also propose a new end-to-end training method that learns better decompositions by leveraging \ICG, and optionally IIW and SAW, two recent datasets of sparse annotations on real-world images. Surprisingly, we find that a decomposition network trained solely on our synthetic data outperforms the state-of-the-art on both IIW and SAW, and performance improves even further when IIW and SAW data is added during training. Our work demonstrates the suprising effectiveness of carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering' published in ECCV, 201

Color in context and spatial color computation

The purpose of this dissertation is to contribute in the field of spatial color computation models.We begin introducing an overview about different approaches in the definitionof computational models of color in digital imaging. In particular, we present a recent accurate mathematical definition of the Retinex algorithm, that lead to the definition of a new computational model called Random Spray Retinex (RSR). We then introduce the tone mapping problem, discussing the need for color computation in the implementation of a perceptual correct computational model. At this aim we will present the HDR Retinex algorithm, that addresses tone mappingand color constancy at the same time. In the end, we present some experiments analyzing the influence of HDR Retinex spatial color computation on tristimulus colors obtained using different Color Matching Functions (CMFs) on spectral luminance distribution generated by a photometric raytracer

Video Enhancement and Dynamic Range Control of HDR Sequences for Automotive Applications

CMOS video cameras with high dynamic range (HDR) output are particularly suitable for driving assistance applications, where lighting conditions can strongly vary, going from direct sunlight to dark areas in tunnels. However, common visualization devices can only handle a low dynamic range, and thus a dynamic range reduction is needed. Many algorithms have been proposed in the literature to reduce the dynamic range of still pictures. Anyway, extending the available methods to video is not straightforward, due to the peculiar nature of video data. We propose an algorithm for both reducing the dynamic range of video sequences and enhancing its appearance, thus improving visual quality and reducing temporal artifacts. We also provide an optimized version of our algorithm for a viable hardware implementation on an FPGA. The feasibility of this implementation is demonstrated by means of a case study

Tone mapping for high dynamic range images

Tone mapping is an essential step for the reproduction of "nice looking" images. It provides the mapping between the luminances of the original scene to the output device's display values. When the dynamic range of the captured scene is smaller or larger than that of the display device, tone mapping expands or compresses the luminance ratios. We address the problem of tone mapping high dynamic range (HDR) images to standard displays (CRT, LCD) and to HDR displays. With standard displays, the dynamic range of the captured HDR scene must be compressed significantly, which can induce a loss of contrast resulting in a loss of detail visibility. Local tone mapping operators can be used in addition to the global compression to increase the local contrast and thus improve detail visibility, but this tends to create artifacts. We developed a local tone mapping method that solves the problems generally encountered by local tone mapping algorithms. Namely, it does not create halo artifacts, nor graying-out of low contrast areas, and provides good color rendition. We then investigated specifically the rendition of color and confirmed that local tone mapping algorithms must be applied to the luminance channel only. We showed that the correlation between luminance and chrominance plays a role in the appearance of the final image but a perfect decorrelation is not necessary. Recently developed HDR monitors enable the display of HDR images with hardly any compression of their dynamic range. The arrival of these displays on the market create the need for new tone mapping algorithms. In particular, legacy images that were mapped to SDR displays must be re-rendered to HDR displays, taking best advantage of the increase in dynamic range. This operation can be seen as the reverse of the tone mapping to SDR. We propose a piecewise linear tone scale function that enhances the brightness of specular highlights so that the sensation of naturalness is improved. Our tone scale algorithm is based on the segmentation of the image into its diffuse and specular components as well as on the range of display luminance that is allocated to the specular component and the diffuse component, respectively. We performed a psychovisual experiment to validate the benefit of our tone scale. The results showed that, with HDR displays, allocating more luminance range to the specular component than what was allocated in the image rendered to SDR displays provides more natural looking images

Milano Retinex family

Several different implementations of the Retinex model have been derived from the original Land and McCann's paper. This paper aims at presenting the Milano-Retinex family, a collection of slightly different Retinex implementations, developed by the Department of Computer Science of Universit\ue1 degli Studi di Milano. One important difference is in their goals: while the original Retinex aims at modeling vision, the Milano-Retinex family is mainly applied as an image enhancer, mimicking some mechanisms of the human vision system