Attila Neumann

Global contrast factor - a new approach to image contrast

Contrast in image processing is usually defined as a ratio between the darkest and the brightest spots of an image. In this paper we introduce a different contrast definition. The newly introduced Global Contrast Factor (GCF) corresponds closer to the human perception of contrast. GCF uses contrasts at various resolution levels in order to compute overall contrast. Experiments were conducted in order to find weight factors needed to calculate GCF. GCF measures richness of detail as perceived by a human observer, and as such can be used in various application areas like rendering, tone mapping, volume visualization, and lighting design.

Color style transfer techniques using hue, lightness and saturation histogram matching

We present new methods which transfer the color style of a source image into an arbitrary given target image having a different 3D color distribution. The color transfer has a high importance ensuring a wide area of applications from artistic transformation of the color atmosphere of images until different scientific visualizations using special gamut mappings. Our technique use a permissive, or optionally strict, 3D histogram matching, similarly to the sampling of multivariable functions applying a sequential chain of conditional probability density functions. We work by order of hue, hue dependent lightness and from both dependent saturation histograms of source and target images, respectively. We apply different histogram transformations, like smoothing or contrast limitation, in order to avoid some unexpected high gradients and other artifacts. Furthermore, we use dominance suppression optionally, by applying sub-linear functions for the histograms in order to get well balanced color distributions, or an overall appearance reflecting the memory color distribution better. Forward and inverse operations on the corresponding cumulative histograms ensure a continuous mapping of perceptual attributes correlating to limited derivatives. Sampling an appropriate fraction of the pixels and using perceptually accurate and continuous histograms with minimal size as well as other gems make this method robust and fast.

Hierarchical Monte Carlo Radiosity

Hierarchical radiosity and Monte Carlo radiosity are branches of radiosity research that focus on complementary problems. The synthesis of both families of radiosity algorithms has however received little attention until now. In this paper, a procedure is presented that bridges the gap. It allows any proposed hierarchical refinement strategy to be investigated in the context of an arbitrary discrete Monte Carlo radiosity algorithm. The synthesis of Monte Carlo radiosity and hierarchical radiosity yields more reliable and easy-to-use radiosity algorithms. Our experiments show that storage requirements for rendering complex models can be reduced to about 20% compared to hierarchical radiosity. At the same time, computation times for images of very reasonable quality can be reduced by one order of magnitude.

The Stochastic Ray Method for Radiosity

This paper solves the system of radiosity equations with a stochastic numerical approach. Due to the high complexity of the problem for highly complex scenes, a stochastic variation of Jacobi iteration is developed which converges stochastically to the correct solution. The new method, called the Stochastic Ray Method, is a significant improvement of Stochastic Radiosity. A large number of independent rays is chosen stochastically by importance sampling of the patches according to their power after the previous iteration step. They all carry an equal amount of power into random directions, thereby representing together the total energy interreflection of the entire environment in a stochastic manner. Assuming a correctly distributed initial solution, which can be reached easily, the iteration process converges quickly and reduces the error in the result faster than other stochastic radiosity approaches. The new algorithm can easily be extended to treat various phenomena which are normally rather costly to incorporate in radiosity environments, perfect specular reflection and specular transmittance, non-diffuse self-emission and point light sources.

Photosimulation interreflection with arbitrary reflection models and illumination

Methods for realistic image synthesis are described. Exact mathematical establishment of reflectance models is presented, together with the development of feasible reflectance models meeting the energy balance. A new bidirectional reflectance type, separable reflectance, is introduced. Necessary and sufficient conditions for the iterative solution of the linear equation system of interreflection are presented. Lastly, a new method of inserting numerically defined objects into real optical surroundings is described.

Radiosity and hybrid methods

We examine various solutions to the global illumination problem, based on an exact mathematical analysis of the rendering equation. In addition to introducing efficient radiosity algorithms, we present a uniform approach to reformulate all of the basic radiosity equations used so far. Using hybrid methods we are able to analyze possible combinations of the view-dependent ray-tracing method and of the low-resolution radiosity-based method, and to offer new algorithms.

Radiosity with Well Distributed Ray Sets

In this paper we present a new radiosity algorithm, based on the notion of a well distributed ray set (WDRS). A WDRS is a set of rays, connecting mutually visible points and patches, that forms an approximate representation of the radiosity operator and the radiosity distribution. We propose an algorithm that constructs an optimal WDRS for a given accuracy and mesh. The construction is based on discrete importance sampling as in previously proposed stochastic radiosity algorithms, and on quasi Monte Carlo sampling. Quasi Monte Carlo sampling leads to faster convergence rates and the fact that the sampling is deterministic makes it possible to represent the well distributed ray set very efficiently in computer memory. Like previously proposed stochastic radiosity algorithms, the new algorithm is well suited for computing the radiance distribution in very complex diffuse scenes, when it is not feasible to explicitly compute and store form factors as in classical radiosity algorithms. Experiments show that the new algorithm is often more efficient than previously proposed Monte Carlo radiosity algorithms by half an order of magnitude and more.

Importance-driven stochastic ray radiosity

The stochastic ray radiosity method [10] is a radiosity method in which no form-factors are computed explicitly. Because of this, the method is very well-suited to compute the radiance distribution in very complex diffuse environments. In this paper we present an extension of this method which will provide a significant reduction of computational cost in cases where accurate knowledge of the illumination is needed in only a small part of the scene. This is accomplished by computing a second quantity, called importance, during the radiance computation. Importance is then used to modulate the patch sampling probabilities in order to obtain lower variance in relevant regions of the scene.

Reflectance Models with Fast Importance Sampling

We introduce a physically plausible mathematical model for a large class of BRDFs. The new model is as simple as the well‐known Phong model, but eliminates its disadvantages. It gives a good visual approximation for many practical materials: coated metals, plastics, ceramics, retro‐reflective paints, anisotropic and retro‐reflective materials, etc. Because of its illustrative properties it can be used easily in most commercial software and because of its low computational cost it is practical for virtual reality. The model is based on a special basic BRDF definition, which meets the requirements of reciprocity and of energy conservation. Then a class of BRDFs is constructed from this basic BRDF with different weight functions. The definition of such weight functions requires the user to specify the profile of the highlights, from which the weight function is obtained by derivation. It is also demonstrated how importance sampling can be used with the new BRDFs.

An interactive perception-based model for characterization of display devices

This paper describes a simple to use, yet accurate way to obtain the Tone Reproduction Curve (TRC) of display devices without the use of a measurement device. Human vision is used to compare a series of dithered color patches against interactively changeable homogeneously colored display areas. Results comparing this method with spectrophotometer measurements are given for three monitors.