A Simple Approach in Digitising a Photographic Collection

This paper reviews the processes involved in the digitisation, display and storage of medium size collections of photographs using simple and inexpensive, commercially available equipment. It is also aimed to provide a guideline for evaluating the performance of such imaging devices on aspects of image quality. A collection of slides, representing first-generation analogue reproductions of a photographic collection from the nineteenth century, is treated as a case study. Constraints on the final image quality and the implications on the digital archive are discussed along with a presentation of device characterisation and calibration procedures. Summary results from objective measurements carried out to assess the systems are presented. The issues of file-format, physical storage and data migration are also addressed

Measurements of the modulation transfer function of image displays

Measurements of the Modulation Transfer Function (MTF) of image displays are often required for objective image quality assessments, but are difficult to carry out due to the need for specialized apparatus. This article presents a simple method for the measurement of the MTF of a sample CRT display system which involves the use of a still digital camera for the acquisition of displayed test targets. Measurements are carried out using, first, the sine wave method, where a number of artificial sine wave images of discrete spatial frequency and constant modulation are captured from a close distance. Fourier techniques are employed to extract the amplitude of the display signal from the resulting macroimages. In a second phase, displayed artificial step edges are captured, and the ISO 12333 SFR (Spatial Frequency Response) Slanted Edge plug-in is used for automatic edge analysis. The display MTF, in both cases, is cascaded from the closed-loop system MTF. The two measuring techniques produced matching results, indicating that under controlled test conditions accurate measurements of the display MTF can be achieved with the use of relatively simple equipment

Predicting image quality using a modular image difference model

The paper is focused on the implementation of a modular color image difference model, as described in [1], with aim to predict visual magnitudes between pairs of uncompressed images and images compressed using lossy JPEG and JPEG 2000. The work involved programming each pre-processing step, processing each image file and deriving the error map, which was further reduced to a single metric. Three contrast sensitivity function implementations were tested; a Laplacian filter was implemented for spatial localization and the contrast masked-based local contrast enhancement method, suggested by Moroney, was used for local contrast detection. The error map was derived using the CIEDE2000 color difference formula on a pixel-by-pixel basis. A final single value was obtained by calculating the median value of the error map. This metric was finally tested against relative quality differences between original and compressed images, derived from psychophysical investigations on the same dataset. The outcomes revealed a grouping of images which was attributed to correlations between the busyness of the test scenes (defined as image property indicating the presence or absence of high frequencies) and different clustered results. In conclusion, a method for accounting for the amount of detail in test is required for a more accurate prediction of image quality

Camera System Performance Derived from Natural Scenes

The Modulation Transfer Function (MTF) is a well-established measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Non-linearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds

Special Section Guest Editorial: Image/Video Quality and System Performance

Rapid developments in display technologies, digital printing, imaging sensors, image processing and image transmission are providing new possibilities for creating and conveying visual content. In an age in which images and video are ubiquitous and where mobile, satellite, and three-dimensional (3-D) imaging have become ordinary experiences, quantification of the performance of modern imaging systems requires appropriate approaches. At the end of the imaging chain, a human observer must decide whether images and video are of a satisfactory visual quality. Hence the measurement and modeling of perceived image quality is of crucial importance, not only in visual arts and commercial applications but also in scientific and entertainment environments. Advances in our understanding of the human visual system offer new possibilities for creating visually superior imaging systems and promise more accurate modeling of image quality. As a result, there is a profusion of new research on imaging performance and perceived quality

Scene classification with respect to image quality measurements

Psychophysical image quality assessments have shown that subjective quality depended upon the pictorial content of the test images. This study is concerned with the nature of scene dependency, which causes problems in modeling and predicting image quality. This paper focuses on scene classification to resolve this issue and used K-means clustering to classify test scenes. The aim was to classify thirty two original test scenes that were previously used in a psychophysical investigation conducted by the authors, according to their susceptibility to sharpness and noisiness. The objective scene classification involved: 1) investigation of various scene descriptors, derived to describe properties that influence image quality, and 2) investigation of the degree of correlation between scene descriptors and scene susceptibility parameters. Scene descriptors that correlated with scene susceptibility in sharpness and in noisiness are assumed to be useful in the objective scene classification. The work successfully derived three groups of scenes. The findings indicate that there is a potential for tackling the problem of sharpness and noisiness scene susceptibility when modeling image quality. In addition, more extensive investigations of scene descriptors would be required at global and local image levels in order to achieve sufficient accuracy of objective scene classification

A case study in digitizing a photographic collection

This paper reviews the processes involved in the digitisation, display and storage of medium size collections of photographs using mid-range commercially available equipment. Guidelines for evaluating the performance of these digitisation processes based on aspects of image quality are provided. A collection of photographic slides, representing first-generation analogue reproductions of a photographic collection from the nineteenth century, is treated as a case study. Constraints on the final image quality and the implications of digital archiving are discussed. Full descriptions of device characterisation and calibration procedures are given and results from objective measurements carried out to assess the digitisation system are presented. The important issues of file format, physical storage and data migration are also addressed

Preferred Tone Reproduction of Images on Soft Displays

A series of psychophysical experiments have been carried out to investigate the preferred subjective contrast of displayed images. The contrast of the displayed images was evaluated with the use of a gamma model which involved cascading the gamma values of the individual components of the imaging system. The preferred contrast was found to be scene dependent and varied between 1.56 and 1.96 when images where viewed in total darkness. Probit analysis on the distributions of preferred gamma values showed that the probits of those scenes which had extremes in contrast were outside the confidence limits of other more representative scenes. The validity and implications of applying the gamma model to determine the tone reproduction of an imaging system has been investigated

Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply non- linear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The scene- and-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-process- dependent Modulation Transfer Functions (SPD-MTF)

Perceptual image attribute scales derived from overall image quality assessments

Psychophysical scaling is commonly based on the assumption that the overall quality of images is based on the assessment of individual attributes which the observer is able to recognise and separate, i.e. sharpness, contrast, etc. However, the assessment of individual attributes is a subject of debate, since they are unlikely to be independent from each other. This paper presents an experiment that was carried to derive individual perceptual attribute interval scales from overall image quality assessments, therefore examine the weight of each individual attribute to the overall perceived quality. A psychophysical experiment was taken by fourteen observers. Thirty two original images were manipulated by adjusting three physical parameters that altered image blur, noise and contrast. The data were then arranged by permutation, where ratings for each individual attribute were averaged to examine the variation of ratings in other attributes. The results confirmed that one JND of added noise and one JND of added blurring reduced image quality more than did one JND in contrast change. Furthermore, they indicated that the range of distortion that was introduced by blurring covered the entire image quality scale but the ranges of added noise and contrast adjustments were too small for investigating the consequences in the full range of image quality. There were several interesting tradeoffs between noise,blur and changes in contrast. Further work on the effect of (test) scene content was carried out to objectively reveal which types of scenes were significantly affected by changes in each attribute