The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy.
Computational Aesthetics applied on digital photography is becoming an interesting issue in different frameworks
(e.g., photo album summarization, imaging acquisition devices). Although it is widely believed and can often be
experimentally demonstrated that aesthetics is mainly subjective, we aim to find some formal or mathematical
explanations of aesthetics in photographs. We propose a scoring function to give an aesthetic evaluation of
digital portraits and group pictures, taking into account faces aspect ratio, their perceptual goodness in terms
of lighting of the skin and their position. Also well-known composition rules (e.g., rule of thirds) are considered
especially for single portrait. Both subjective and quantitatively experiments have confirmed the effectiveness of
the proposed methodology.
The illuminant estimation has an important role in many domain applications such as digital still cameras and mobile phones, where the final image quality could be heavily affected by a poor compensation of the ambient illumination effects. In this paper we present an algorithm, not dependent on the acquiring device, for illuminant estimation and compensation directly in the color filter array (CFA) domain of digital still cameras. The algorithm proposed takes into account both chromaticity and intensity information of the image data, and performs the illuminant compensation by a diagonal transform. It works by combining a spatial segmentation process with empirical designed weighting functions aimed to select the scene objects containing more information for the light chromaticity estimation. This algorithm has been designed exploiting an experimental framework developed by the authors and it has been evaluated on a database of real scene images acquired in different, carefully controlled, illuminant conditions. The results show that a combined multi domain pixel analysis leads to an improvement of the performance when compared to single domain pixel analysis.
An automatic natural scenes classifier and enhancer is presented. It works mainly by combining chromatic and positional criterions in order to classify and enhance portraits and landscapes natural scenes images. Various image processing applications can easily take advantage from the proposed solution, e.g. automatically drive camera settings for the optimization of exposure, focus, or shutter speed parameters, or post processing applications for color rendition optimization. A large database of high quality images has been used to design and tune the algorithm, according to wide accepted assumptions that few chromatic classes on natural images have the most perceptive impact on the human visual system. These are essentially skin, vegetation and sky?sea. The adaptive color rendition technique, which has been derived from the results produced by the image classifier, is based on a simple yet effective principle: it shifts the chromaticity of the regions of interest towards the statistically expected ones. Introduction of disturbing color artifacts is avoided by a proper modulation and by preservation of original image luminance values. Quantitative results obtained over an extended data set not belonging to the training database, show the effectiveness of the solution proposed both for the natural image classification and the color enhancement techniques.