Small digital camera modules such as those in mobile phones have become ubiquitous. Their low-light performance is of
utmost importance since a high percentage of images are made under low lighting conditions where image quality failure
may occur due to blur, noise, and/or underexposure. These modes of image degradation are not mutually exclusive: they
share common roots in the physics of the imager, the constraints of image processing, and the general trade-off situations
in camera design. A comprehensive analysis of failure modes is needed in order to understand how their interactions
affect overall image quality.
Low-light performance is reported for DSLR, point-and-shoot, and mobile phone cameras. The measurements target
blur, noise, and exposure error. Image sharpness is evaluated from three different physical measurements: static spatial
frequency response, handheld motion blur, and statistical information loss due to image processing. Visual metrics for
sharpness, graininess, and brightness are calculated from the physical measurements, and displayed as orthogonal image
quality metrics to illustrate the relative magnitude of image quality degradation as a function of subject illumination. The
impact of each of the three sharpness measurements on overall sharpness quality is displayed for different light levels.
The power spectrum of the statistical information target is a good representation of natural scenes, thus providing a
defined input signal for the measurement of power-spectrum based signal-to-noise ratio to characterize overall imaging
performance.
KEYWORDS: Signal to noise ratio, Imaging systems, Cameras, Reliability, Interference (communication), Image compression, Signal detection, LCDs, Projection systems, Scene simulation
Mobile applications present new image quality challenges. Automotive vision requires reliable capture of scene detail. Photospace measurements have shown that the extremely wide intrascene dynamic range of traffic scenes necessitates wide-dynamic-range (WDR) technology. Multiple-slope complementary metal-oxide semiconductor (CMOS) technology adaptively extends dynamic range by partially resetting the pixel, resulting in a response curve with piecewise linear slopes of progressively increasing compression. As compression and thus dynamic range increase, a trade-off against detail loss is observed. Incremental signal-to-noise ratio (iSNR) has been proposed in ISO/TC42 standards for determining dynamic range, and this work describes how to adapt these to WDR. Measurements and computer simulations reveal that the observed trade-off between WDR extension and the loss of local detail can be explained by a drop in iSNR at each reset point. If a reset is not timed optimally, then iSNR may drop below the detection limit causing an iSNR hole to appear within the dynamic range. Thus iSNR has extended utility: it not only determines the dynamic range limits but also defines dynamic range as the luminance range where detail detection is reliable. It has become the critical criterion when maximizing dynamic range to maintain the minimum necessary level of detection reliability.
KEYWORDS: Imaging systems, Signal detection, Signal to noise ratio, Cameras, Reliability, Interference (communication), LCDs, Image quality, Image compression, Video
In the emerging field of automotive vision, video capture is the critical front-end of driver assistance and active safety
systems. Previous photospace measurements have shown that light levels in natural traffic scenes may contain an
extremely wide intra-scene intensity range. This requires the camera to have a wide dynamic range (WDR) for it to adapt
quickly to changing lighting conditions and to reliably capture all scene detail.
Multiple-slope CMOS technology offers a cost-effective way of adaptively extending dynamic range by partially
resetting (recharging) the CMOS pixel once or more often within each frame time. This avoids saturation and leads to a
response curve with piecewise linear slopes of progressively increasing compression.
It was observed that the image quality from multiple-slope image capture is strongly dependent on the control (height
and time) of each reset barrier. As compression and thus dynamic range increase there is a trade-off against contrast and
detail loss.
Incremental signal-to-noise ratio (iSNR) is proposed in ISO 15739 for determining dynamic range. Measurements and
computer simulations revealed that the observed trade-off between WDR extension and the loss of local detail could be
explained by a drop in iSNR at each reset point. If a reset barrier is not optimally placed then iSNR may drop below the
detection limit so that an 'iSNR hole' appears in the dynamic range. Thus ISO 15739 iSNR has gained extended utility:
it not only measures the dynamic range limits but also defines dynamic range as the intensity range where detail
detection is reliable. It has become a critical criterion when designing adaptive barrier control algorithms that maximize
dynamic range while maintaining the minimum necessary level of detection reliability.
Photospace data previously measured on large image sets have shown that a high percentage of camera phone pictures
are taken under low-light conditions. Corresponding image quality measurements linked the lowest quality to these
conditions, and subjective analysis of image quality failure modes identified image blur as the most important
contributor to image quality degradation.
Camera phones without flash have to manage a trade-off when adjusting shutter time to low-light conditions. The shutter
time has to be long enough to avoid extreme underexposures, but not short enough that hand-held picture taking is still
possible without excessive motion blur. There is still a lack of quantitative data on motion blur. Camera phones often do
not record basic operating parameters such as shutter speed in their image metadata, and when recorded, the data are
often inaccurate. We introduce a device and process for tracking camera motion and measuring its Point Spread Function
(PSF). Vision-based metrics are introduced to assess the impact of camera motion on image quality so that the low-light
performance of different cameras can be compared. Statistical distributions of user variability will be discussed.
It is a myth that more pixels alone result in better images. The marketing of camera phones in particular has focused on
their pixel numbers. However, their performance varies considerably according to the conditions of image capture.
Camera phones are often used in low-light situations where the lack of a flash and limited exposure time will produce
underexposed, noisy and blurred images. Camera utilization can be quantitatively described by photospace distributions,
a statistical description of the frequency of pictures taken at varying light levels and camera-subject distances. If the
photospace distribution is known, the user-experienced distribution of quality can be determined either directly by direct
measurement of subjective quality, or by photospace-weighting of objective attributes.
The population of a photospace distribution requires examining large numbers of images taken under typical camera
phone usage conditions. ImagePhi was developed as a user-friendly software tool to interactively estimate the primary
photospace variables, subject illumination and subject distance, from individual images. Additionally, subjective
evaluations of image quality and failure modes for low quality images can be entered into ImagePhi.
ImagePhi has been applied to sets of images taken by typical users with a selection of popular camera phones varying in
resolution. The estimated photospace distribution of camera phone usage has been correlated with the distributions of
failure modes. The subjective and objective data show that photospace conditions have a much bigger impact on image
quality of a camera phone than the pixel count of its imager. The 'megapixel myth' is thus seen to be less a myth than an
ill framed conditional assertion, whose conditions are to a large extent specified by the camera's operational state in
photospace.
Although its lens and image sensor fundamentally limit a digital still camera's imaging performance, image processing
can significantly improve the perceived quality of the output images. A well-designed processing pipeline achieves a
good balance between the available processing power and the image yield (the fraction of images that meet a minimum
quality criterion).
This paper describes the use of subjective and objective measurements to establish a methodology for evaluating the
image quality of processing pipelines. The test suite contains images both of analytical test targets for objective
measurements, and of scenes for subjective evaluations that cover the photospace for the intended application.
Objective image quality metrics correlating with perceived sharpness, noise, and color reproduction were used to
evaluate the analytical images. An image quality model estimated the loss in image quality for each metric, and the
individual metrics were combined to estimate the overall image quality. The model was trained with the subjective
image quality data.
The test images were processed through different pipelines, and the overall objective and subjective data was assessed
to identify those image quality metrics that exhibit significant correlation with the perception of image quality. This
methodology offers designers guidelines for effectively optimizing image quality.
For more than thirty years imaging scientists have constructed metrics to predict psychovisually perceived image quality. Such metrics are based on a set of objectively measurable basis functions such as Noise Power Spectrum (NPS), Modulation Transfer Function (MTF), and characteristic curves of tone and color reproduction. Although these basis functions constitute a set of primitives that fully describe an imaging system from the standpoint of information theory, we found that in practical imaging systems the basis functions themselves are determined by system-specific primitives, i.e. technology parameters. In the example of a printer, MTF and NPS are largely determined by dot structure. In addition MTF is determined by color registration, and NPS by streaking and banding. Since any given imaging system is only a single representation of a class of more or less identical systems, the family of imaging systems and the single system are not described by a unique set of image primitives. For an image produced by a given imaging system, the set of image primitives describing that particular image will be a singular instantiation of the underlying statistical distribution of that primitive. If we know precisely the set of imaging primitives that describe the given image we should be able to predict its image quality. Since only the distributions are known, we can only predict the distribution in image quality for a given image as produced by the larger class of 'identical systems'. We will demonstrate the combinatorial effect of the underlying statistical variations in the image primitives on the objectively measured image quality of a population of printers as well as on the perceived image quality of a set of test images. We also will discuss the choice of test image sets and impact of scene content on the distribution of perceived image quality.
The s-CIELAB color difference metric combines the standard CIELAB metric for perceived color difference with spatial contrast sensitivity filtering. When studying the performance of digital image processing algorithms, maps of spatial color difference between 'before' and 'after' images are a measure of perceived image difference. A general image quality metric can be obtained by modeling the perceived difference from an ideal image.
This paper explores the s-CIELAB concept for evaluating the quality of digital prints. Prints present the challenge that the 'ideal print' which should serve as the reference when calculating the delta E* error map is unknown, and thus be estimated from the scanned print. A reasonable estimate of what the ideal print 'should have been' is possible at least for images of known content such as flat fields or continuous wedges, where the error map can be calculated against a global or local mean.
While such maps showing the perceived error at each pixel are extremely useful when analyzing print defects, it is desirable to statistically reduce them to a more manageable dataset. Examples of digital print uniformity are given, and the effect of specific print defects on the s-CIELAB delta E* metric are discussed.
INCITS W1 is the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment. In September 2000, INCITS W1 was chartered to develop an appearance-based image quality standard. The resulting W1.1 project is based on a proposal that perceived image quality could be described by a small set of broad-based attributes. There are currently five ad hoc W1.1 teams, each working on one or more of these image quality attributes. This paper summarizes the work of the W1.1 Microuniformity ad hoc team. The agreed-upon process for developing the W1.1 Image Quality of Printers standards is described in a statement located on the INCITS W1.1 web site (ncits.org/tc_home/w11htm/incits_w11.htm), and the process schematic is reproduced here as Figure 1, (in which a final, independent confirmation step has been excluded for brevity).
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