Quantitative measurement of contrast, texture, color, and noise for digital photography of high dynamic range scenes
Today, most advanced mobile phone cameras integrate multi image technologies such as high dynamic range (HDR) imaging. The objective of HDR imaging is to overcome some of the limitations imposed by the sensor physics, which limit the performance of small camera sensors used in mobile phones compared to larger sensors used in digital single lens reflex (DSLR) cameras. In this context, it becomes more and more important to establish new image quality measurement protocols and test scenes that can differentiate the image quality performance of these devices. In this work, we describe image quality measurements for HDR scenes covering local contrast preservation, texture preservation, color consistency, and noise stability. By monitoring these four attributes in both the bright and dark parts of the image, over different dynamic ranges, we benchmarked four leading smartphone cameras using different technologies and contrasted the results with subjective evaluations. Introduction Despite the very tight constraint on form factor, the smartphone camera industry has seen big improvements on image quality in the last few years. To comfortably fit in our pockets, smartphone thickness is limited to a few millimeters. This limits the pixel size and the associated full well capacity, which in turn reduces its dynamic range. The use of multi image technologies is one of the key contributors to the image quality improvement in the last years. It allows to overcome the limitation of small sensors by combining multiple images taken simultaneously (with multiple sensors, multiple cameras) or sequentially (using bracketed exposures, bursts) Multi image processing enables many computational photography applications including spatial or temporal noise reduction [9] HDR tone mapping [15, 8, 14] motion blur reduction [10, 11] super resolution, focus stacking, and depth of field manipulation [25] among others. The creation of a single image from a sequence of images entails several problems related to the motion in the scene or the camera. We refer to [5, 7, 1] and references therein for a review of methods for evaluating and dealing with these issues. In a previous work [1] we explored these artifacts and provided a first approach to evaluating multi image algorithms. In this work we focus on the evaluation of the tone mapping of HDR scenes. Since the images must be displayed on screens with limited dynamic range, the tone mapping algorithm becomes a critical part of the system [14] This process is qualitative in nature as it aims at tricking the observer into thinking that the image shown on a low dynamic range medium has actually a high dynamic range [4, 12] Nevertheless, as we will see below, the quantitative assessment of some attributes is possible and it fits with our perception of the C on su m er