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 an image; it does not take into consideration many important camera qualities such as color, signal to noise ratio (SNR), or sharpness. Additionally, most of today's megapixel counts run well in excess of the minimum required for detail in printing or viewing. Because these higher megapixel counts are not necessarily associated with higher quality or larger sensors, this metric is poor even as a description of image detail alone.

Background

The International Standards Organization (ISO), recognizing the limitations of existing metrics, has started the I3A Camera Phone Image Quality (CPIQ) initiative. The goal is to develop a relevant set of camera metrics which correspond to perceived image quality by humans. To do so, metrics have been developed which measure the spatial resolution, noise and color accuracy of mobile phone cameras.

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). This number represents the Euclidean distance between two colors in a Lab color space.

Methods

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

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Conclusions

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

References

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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.