Estimating Print Quality Attributes by Image Quality Metrics

Image quality assessment is a difficult and complex task due to its subjectivity and dimensionality. Attempts have been made to make image quality assessment more objective, such as the introduction of image quality metrics. However, it has been proven difficult to create an image quality metric correlated with perceived overall image quality. Because of this, and to reduce the dimensionality, quality attributes have been proposed to help linking subjective and objective image quality. Recently, Pedersen et al. (CIC, 2009) proposed a set of meaningful quality attributes for the evaluation of color prints with the intention of being used with image quality metrics. In this paper we evaluate image quality metrics for the quality attributes, and propose a set of suitable image quality metrics for each attribute. The experimental results indicate that the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the most suitable metric for measuring the sharpness quality attribute. For the other quality attributes the results are not as conclusive. Introduction The printing industry is continuously moving forward as new products are introduced to the market. These products are becoming more and more affordable, and the technology is constantly improved. The need to assess the quality is also increased, for example to verify that new technology advancements produce higher quality prints than the current technology. There are two main methods to assess Image Quality (IQ), subjective and objective. Subjective assessment is carried out by human observers. Objective assessment does not involve human observers, but rather measurement devices to obtain numerical values, or alternatively IQ metrics. These IQ metrics are usually developed to take into account the human visual system, and thus with the goal of being correlated with subjective assessment. Numerous IQ metrics have been proposed [1], but so far no one has succeeded proposing an IQ metric fully correlated with subjective IQ [2–5]. Mostly because IQ is multi-dimensional and very complex. To reduce the complexity and dimensionality, Quality Attributes (QAs) have been used in the assessment of IQ. These QAs are terms of perception [6], such as sharpness and saturation. In earlier papers [7, 8] we proposed a set of six QAs for the evaluation of color prints: • Color contains aspects, such as hue, saturation, and color rendition, except lightness. • Lightness will range from ”light” to ”dark”. • Contrast can be described as the perceived magnitude of visually meaningful differences, global and local, in lightness and chromaticity, within the image. • Sharpness is related to the clarity of details and definition of edges. • Artifacts, like noise, contouring, and banding, contribute to degrading the quality of an image if detectable. • The physical QA contains all physical parameters that affect quality, such as paper properties and gloss. These QAs are referred to as the Color Printing Quality Attributes (CPQAs). We have created the CPQAs to help establishing a link between subjective and objective evaluation. Our long term goal is to evaluate quality without involving human observers. In order to achieve this, with the starting point of CPQAs, we need to identify IQ metrics able to correctly measure each CPQA. Therefore, in this paper we investigate and evaluate IQ metrics in the context of CPQAs, with the goal of proposing suitable metrics for each of the CPQAs. To achieve our goal the first step is to identify relevant IQ metrics for each of the CPQAs. Then an experiment is set up to evaluate each of the CPQAs, where both naive and expert observers are included to assure an extensive evaluation. Later, the results from the relevant metrics identified in the first step are compared against the results of the two observer groups. This enables us to refine the selection of IQ metrics for each CPQA, and to recommend a suitable set of IQ metrics able to measure each of the CPQAs. This paper is organized as follows: First we select the relevant metrics for the different CPQAs. Then the experimental setup is explained, and the printed images are prepared for the IQ metrics. We then evaluate the metrics before we conclude and propose future work. Selection of Image Quality Metrics for the Color Printing Quality Attributes Numerous IQ metrics have been proposed in the literature [1], and we have selected a sub-set of these, as shown in Table 1. The selection is based on the results from previous evaluation [2–4], the criteria on which the metrics were created, and their popularity. Since many of the IQ metrics are not created to evaluate all aspects of IQ, only the suitable metrics for each CPQA will be evaluated. Furthermore, for specific CPQAs we also evaluate parts of the metrics. For example, S-CIELAB combines the lightness and color differences to obtain an overall value. When suitable, we will evaluate these separately in addition to the full metric. Experimental setup In this paper, two experimental phases were carried out. In the first phase, 15 naive observers judged overall quality and the different CPQAs on a set of images. In the second phase, four expert observers judged the quality of a set of images and elaborated on different quality issues. We will give a brief introduction of the experimental setup, for more information see Pedersen et al. [18]. Table 1: Selected IQ metrics for the evaluation of CPQAs. X X X X X X X X X Metric CPQA Sharpness Color Lightness Contrast Artifacts