Tone mapping

About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.

Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

Abstract: The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in $$\hbox {O}(N^2K)$$O(N2K) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.

Efficient visual system processing of spatial and luminance statistics in representational and non-representational art

Abstract: An emerging body of research suggests that artists consistently seek modes of representation that are efficiently processed by the human visual system, and that these shared properties could leave statistical signatures. In earlier work, we showed evidence that perceived similarity of representational art could be predicted using intensity statistics to which the early visual system is attuned, though semantic content was also found to be an important factor. Here we report two studies that examine the visual perception of similarity. We test a collection of non-representational art, which we argue possesses useful statistical and semantic properties, in terms of the relationship between image statistics and basic perceptual responses. We find two simple statistics-both expressed as single values-that predict nearly a third of the overall variance in similarity judgments of abstract art. An efficient visual system could make a quick and reasonable guess as to the relationship of a given image to others (i.e., its context) by extracting these basic statistics early in the visual stream, and this may hold for natural scenes as well as art. But a major component of many types of art is representational content. In a second study, we present findings related to efficient representation of natural scene luminances in landscapes by a well-known painter. We show empirically that elements of contemporary approaches to high-dynamic range tone-mapping-which are themselves deeply rooted in an understanding of early visual system coding-are present in the way Vincent Van Gogh transforms scene luminances into painting luminances. We argue that global tone mapping functions are a useful descriptor of an artist's perceptual goals with respect to global illumination and we present evidence that mapping the scene to a painting with different implied lighting properties produces a less efficient mapping. Together, these studies suggest that statistical regularities in art can shed light on visual processing.

Method for improved contrast mapping of digital images

Abstract: A method of local contrast mapping that changes the dynamic range of an original image to more closely match the dynamic range of the medium used for the reproduction. The method compresses large contrast differences between different areas of an image while preserving small contrast differences between different areas of an image.

Optimized tone mapping with LDR image quality constraint for backward-compatible high dynamic range image and video coding

Abstract: Backward compatibility to low dynamic range (LDR) displays is an important requirement for high dynamic range (HDR) image and video coding in order to enable a successful transition to HDR technology. In a recent work [1], an optimized solution for tone mapping and inverse tone mapping of HDR images is achieved in terms of mean square error (MSE) of the logarithm of luminance values of HDR image pixels for backward-compatible compression. Although this pioneer optimization approach provides a well settled mathematical framework for tone mapping, one of its important shortcomings is not to take the quality of the resulting LDR images into account during the formulation. In this paper, we include the LDR image quality as a constraint to optimization problem and develop a methodology to compromise the trade-off between HDR image quality and LDR image quality during HDR image and video coding. The developed methodology is verified on HDR images by showing the increase (decrease) in the quality of generated LDR images while losing (gaining) from the rate-distortion performance of HDR image coding.

Bottom-up segmentation for ghost-free reconstruction of a dynamic scene from multi-exposure images

Abstract: High Dynamic Range (HDR) imaging requires one to composite multiple differently exposed images of a scene in the irradiance domain and perform tone mapping of the generated HDR image for displaying on Low Dynamic Range (LDR) devices. In the case of dynamic scenes, standard techniques may introduce artifacts called ghosts if the scene changes are not accounted for. In this paper, we consider the HDR problem for dynamic scenes. We develop a novel bottom-up segmentation algorithm through superpixel grouping which would enable us to detect scene changes. We then employ a piecewise patch-based compositing methodology to directly generate the ghost-free LDR image of the dynamic scene. The primary advantage of our approach is that we do not assume any knowledge of camera response function and exposure settings. Further, our approach performs well even in the case of significant scene changes.