WangMoreno

Introduction

Over the past decade, the market for compact digital cameras has slowly eroded in light of improvements in mobile phone camera technology. However, consumers in the market for a new mobile phone face a difficult challenge when attempting to compare the quality of different cameras. While other components such as battery and processor have relatively clear-cut metrics, such as hours of battery life and frequency/number of cores, there are no such metrics accurately representing camera quality of phones across the market.

Traditionally, consumers looked to the megapixel count as a measure of image quality. In the past, with many digital cameras in the 1-2 megapixel range, this metric could mean the difference between a pixelated photo print and a clear one. However, megapixel count is a very poor measure of the perceived quality of the images produced by a camera; it does not take into consideration many important camera qualities such as color accuracy, signal to noise ratio (SNR), or sharpness. Additionally, with many manufacturers pushing higher and higher megapixel counts, most of today's megapixel counts run well in excess of the amount required for detail in printing or viewing. And yet, because many of these same manufacturers are not pairing these with higher quality or larger sensors, this metric has become poor even as a description of image detail and printable size alone.

Background

The International Standards Organization (ISO), recognizing the limitations of existing metrics, has started the I3A Camera Phone Image Quality (CPIQ) initiative. This development is detailed in the paper Development of the I3A CPIQ spatial metrics[1]. The goal of the initiative is to develop a relevant set of camera metrics which correspond to subjective perceived image quality. To do so, metrics have been developed which measure the spatial resolution, noise, and color accuracy of mobile phone cameras. These metrics attempt to capture the differences discernible to humans viewing the images on a computer display or paper printout, while ignoring the qualities that do not correspond well to perceived quality.

In our project, we are primarily focusing on color accuracy. The measure for color accuracy which we are using is the International Commission on Illumination (CIE) distance metric ΔE* (Delta E). In the image below, the CIE XYZ chromaticity diagram can be seen. The XYZ color space was originally developed in 1931 where Y is a luminance, and combinations of X and Z represent all possible chromaticities. The Lab (L*, a*, b*) color space was developed by the CIE in 1976, which was derived from the prior XYZ color space with the intention of being more perceptually uniform. In Lab space, colors are again represented through three dimensions: L for lightness, and a and b for opposing dimensions of color. Each of these values for a color can be easily computed from their corresponding XYZ values.

The metric ΔE* represents the Euclidean distance between two colors in a Lab color space, calculated from their L*, a*, and b* values via the following formula:

$\Delta E^{*}=\sqrt{(L_2^{*}-L_1^{*}{)}^2+(a_2^{*}-a_1^{*}{)}^2+(b_2^{*}-b_1^{*}{)}^2}$

In this project, we will explore how effective ΔE* is as a measure of color accuracy to compare between different cameras. We will also look at the subjective appearance of various images as viewed by humans on a display, and how well a camera's ΔE* value correlates with perceived image quality.

Methods

Our methodology essentially consisted of two steps:

1. Using various combinations of sensor and image processor properties, find the ΔE* values of various cameras as measured on a standardized color chart.
2. Take

(Todo)

Describe techniques you used to measure and analyze. Describe the instruments, and experimental procedures in enough detail so that someone could repeat your analysis. What software did you use? What was the idea of the algorithms and data analysis?

Results

Organize your results in a good logical order (not necessarily historical order). Include relevant graphs and/or images. Make sure graph axes are labeled. Make sure you draw the reader's attention to the key element of the figure. The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.

Conclusions

Describe what you learned. What worked? What didn't? Why? What should someone next year try?

References

Daxter, Donald; Frederic Cao; Henrik Eliasson; Jonathan Phillips. "Development of the I3A CPIQ spatial metrics." Web. 8 Mar 2014. <http://proceedings.spiedigitallibrary.org/data/Conferences/SPIEP/64097/829302_1.pdf>.

Appendix I

Upload source code, test images, etc, and give a description of each link. In some cases, your acquired data may be too large to store practically. In this case, use your judgement (or consult one of us) and only link the most relevant data. Be sure to describe the purpose of your code and to edit the code for clarity. The purpose of placing the code online is to allow others to verify your methods and to learn from your ideas.

Appendix II

(for groups only) - Work breakdown. Explain how the project work was divided among group members.