A Study on Image Quality Evaluation In Image Capture and Production Process (2016)

The aim of our research is to study varies image quality problems associated with the image capture and produce process. The study of these problems is increased with the growth of new imaging technologies in varies areas (e.g. camera, printer and scanner). In this dissertation, we describe the work we carried out in studying several image quality problems in the image capture and produce process. First, we describe the work carried out to use image quality ruler for the evaluation of the quality of printed pages by laser printer. Assessment of macro-uniformity is important for the development and manufacture of printer products. Our goal is to develop a predictor that will predict macro-uniformity, as judged by human subjects, by scanning and analyzing printed pages. We have implemented the image quality ruler, based on the recommendations of the INCITS W1.1 macro-uniformity team. Our results suggest that the image quality ruler method provides a reliable means to assess macro-uniformity. We then defined and implemented separate features to measure graininess, mottle, large area variation, jitter, and large-scale non-uniformity. Finally, we used these features computed for a set of test pages and the subjects' image quality ruler assessments of these pages to train the two different predictors, we confirmed the efficacy of our predictor. In the next part, we describe a method to help quantify image quality results from psychophysical experiment. Quantifying image quality through subjective evaluation is very critical to image quality evaluation. Using the image quality ruler method, an average score per stimulus can be easily obtained in the unit of Just Noticeable Differences (JNDs). However, it requires a large number of subjects, since pure averaging does not consider the different judging quality of different subjects. In this part, we describe an image quality evaluation framework using the image quality ruler method with a statistical model. From our experimental results, we show that our method provides reliable results without using a large number of subjects. Preliminary results also demonstrate that the estimates of the parameters can guide us to better distribute the valuable human resources used to conduct psychophysical experiments. Next, we focus on the quality problems in the process of color reproduction. For printers, 3D color lookup tables (CLUTs) are used to convert input device-independent colors to out device-dependent CMYK ink. It is desirable to compress CLUTs prior to storage and restore tables as required. In this part, we investigate methods for lossy compression of CLUTs. We demonstrate that through suitable pre-processing of data, significant improvements in compression performance can be obtained as compared to a direct application of lossless data compression methods. In particular, the gains in performance with the proposed methods are accomplished by exploiting the color sensitivity of CLUT data and utilizing 3D DCT and re-ordering for the pre-processing. Finally, we shift topic to input image quality problems (capture quality). We describe a method to measure scanner modulation transfer function (MTF) using a self-printed target. Our goal is to develop an autonomous capability for diagnosis of printer and scanner caused defects with mid-range laser multifunction printers (MFPs), so as to reduce warranty costs. In this part, we consider evaluation of the scanner performance by measuring its MTF. The MTF is a fundamental tool for assessing the performance of imaging systems. Several ways have been proposed to measure the MTF, all of which require a special target, for example a slanted-edge target. We propose algorithms which could use a self-printed slanted-edge target to reduce cost. Our experimental results shows that MTF measurement using self- printed targets is comparable to the results obtained with standard targets.