This PDF file contains the front matter associated with SPIE Proceedings Volume 9016, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

An evaluation of the change in perceived image contrast with changes in displayed image size was carried out. This was achieved using data from four psychophysical investigations, which employed techniques to match the perceived contrast of displayed images of five different sizes. A total of twenty-four S-shape polynomial functions were created and applied to every original test image to produce images with different contrast levels. The objective contrast related to each function was evaluated from the gradient of the mid-section of the curve (gamma). The manipulation technique took into account published gamma differences that produced a just-noticeable-difference (JND) in perceived contrast. The filters were designed to achieve approximately half a JND, whilst keeping the mean image luminance unaltered. The processed images were then used as test series in a contrast matching experiment. Sixty-four natural scenes, with varying scene content acquired under various illumination conditions, were selected from a larger set captured for the purpose. Results showed that the degree of change in contrast between images of different sizes varied with scene content but was not as important as equivalent perceived changes in sharpness ¹.

This paper presents secondary Standard Quality Scale (SQS2) rankings in overall quality JNDs for a subjective analysis of the 3 axes of noise, amplitude, spectral content, and noise type, based on the ISO 20462 softcopy ruler protocol. For the initial pilot study, a Python noise simulation model was created to generate the matrix of noise masks for the softcopy ruler base images with different levels of noise, different low pass filter noise bandwidths and different band pass filter center frequencies, and 3 different types of noise: luma only, chroma only, and luma and chroma combined. Based on the lessons learned, the full subjective experiment, involving 27 observers from Google, NVIDIA and STMicroelectronics was modified to incorporate a wider set of base image scenes, and the removal of band pass filtered noise masks to ease observer fatigue. Good correlation was observed with the Aptina subjective noise study. The absence of tone mapping in the noise simulation model visibly reduced the contrast at high levels of noise, due to the clipping of the high levels of noise near black and white. Under the 34-inch viewing distance, no significant difference was found between the luma only noise masks and the combined luma and chroma noise masks. This was not the intuitive expectation. Two of the base images with large uniform areas, ‘restaurant’ and ‘no parking’, were found to be consistently more sensitive to noise than the texture rich scenes. Two key conclusions are (1) there are fundamentally different sensitivities to noise on a flat patch versus noise in real images and (2) magnification of an image accentuates visual noise in a way that is non-representative of typical noise reduction algorithms generating the same output frequency. Analysis of our experimental noise masks applied to a synthetic Macbeth ColorChecker Chart confirmed the color-dependent nature of the visibility of luma and chroma noise.

This paper describes continuing research concerned with the measurement and modeling of human spatial contrast sensitivity and discrimination functions, using complex pictorial stimuli. The relevance of such functions in image quality modeling is also reviewed. Previously^1,2 we presented the choice of suitable contrast metrics, apparatus and laboratory set-up, the stimuli acquisition and manipulation, the methodology employed in the subjective tests and initial findings. Here we present our experimental paradigm, the measurement and modeling of the following visual response functions: i) Isolated Contrast Sensitivity Function (iCSF); Contextual Contrast Sensitivity Function (cCSF); Isolated Visual Perception Function (iVPF); Contextual Visual Perception Function (cVPF). Results indicate that the measured cCSFs are lower in magnitude than the iCSFs and flatter in profile. Measured iVPFs, cVPFs and cCSFs are shown to have similar profiles. Barten’s contrast detection model³ was shown to successfully predict iCSF. For a given frequency band, the reduction, or masking of cCSF compared with iCSF sensitivity is predicted from the linear amplification model (LAM)⁴. We also show that our extension of Barten’s contrast discrimination model^1,5 is capable of describing iVPFs and cVPFs. We finally reflect on the possible implications of the measured and modeled profiles of cCSF and cVPF to image quality modeling.

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

A new method has been developed to address the objective evaluation of perceived resolution of printed matter. To achieve this, a psychophysical experiment has been designed and conducted that ranked typical test prints representing the manifold of printing processes and substrates. A scanner based method has been developed that computes a score value between 0 and 100 termed Fogra L-Score. It is based on the idea to identify a predefined signal in an image (print). The predefined signal is a perfect representation of the “perceived resolution domain” by means of a test target (RIT ConRes-target) that covers systematic variations of the two governing parameters of perceived resolution namely contrast and spatial resolution. The printed images to be evaluated have been scanned and pre-processed. The level of closeness between the reference (digital representation) and the printed matter (scanned print) have been determined by a 2-dimensional normalized cross-correlation (on the CIEL*-channel). The resulting correlation coefficients have been compared against findings of the performed psychophysical experiment. Finally a framework will be presented that also allows for spatial filtering to address different intended viewing distances as well as chromatic test charts. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.

When evaluating printer resolution, addressability is a key consideration. Addressability defines the maximum number of spots or samples within a given distance, independent of the size of the spots when printed. Effective addressability is the addressability demonstrated by the final, printed output. It is the minimum displacement possible between the centers of printed objects. In this paper, we present a measurement procedure for effective addressability that offers an automated way to experimentally determine the addressability of the printed output. It requires printing, scanning, and measuring a test target. The effective addressability test target contains two types of elements, repeated to fill the page: fiducial lines and line segments. The fiducial lines serve as a relative reference for the incremental displacements of the individual line segments, providing a way to tolerate larger-scale physical distortions in the printer. An ordinary reflection scanner captures the printed test target. By rotating the page on the scanner, it is possible to measure effective addressability well beyond the scanner’s sampling resolution. The measurement algorithm computes the distribution of incremental displacements, forming either a unimodal or bimodal histogram. In the latter case, the mean of the second (non-zero) peak indicates the effective addressability. In the former case, the printer successfully rendered the target’s resolution, requiring another iteration of the procedure after increasing the resolution of the test target. The algorithm automatically estimates whether the histogram is unimodal or bimodal and computes parameters describing the quality of the measured histogram. Several experiments have refined the test target and measurement procedure, including two round-robin evaluations by the ISO WG4 committee. Results include an analysis of approximately 150 printed samples. The effective addressability attribute and measurement procedure are included in ISO/IEC TS 29112, a technical specification that describes the objective measurement of printer resolution for monochrome electrophotographic printers.

Uniformity is one of the issues of most critical concern for laser electrophotographic (EP) printers. Typically, full coverage constant-tint test pages are printed to assess uniformity. Exemplary nonuniformity defects include mottle, grain, pinholes, and “finger prints". It is a real challenge to make an overall Print Quality (PQ) assessment due to the large coverage of a letter-size, constant-tint printed test page and the variety of possible nonuniformity defects. In this paper, we propose a novel method that uses a block-based technique to analyze the page both visually and metrically. We use a grid of 150 pixels × 150 pixels ( ¼ inch × ¼ inch at 600-dpi resolution) square blocks throughout the scanned page. For each block, we examine two aspects: behavior of its pixels within the block (metrics of graininess) and behavior of the blocks within the printed page (metrics of nonuniformity). Both ΔE (CIE 1976) and the L* lightness channel are employed. For an input scanned page, we create eight visual outputs, each displaying a different aspect of nonuniformity. To apply machine learning, we train scanned pages of different 100% solid colors separately with the support vector machine (SVM) algorithm. We use two metrics as features for the SVM: average dispersion of page lightness and standard deviation in dispersion of page lightness. Our results show that we can predict, with 83% to 90% accuracy, the assignment by a print quality expert of one of two grades of uniformity in the print.

Print mottle is one of several attributes described in ISO/IEC DTS 24790, a draft technical specification for the measurement of image quality for monochrome printed output. It defines mottle as aperiodic fluctuations of lightness less than about 0.4 cycles per millimeter, a definition inherited from the latest official standard on printed image quality, ISO/IEC 13660. In a previous publication, we introduced a modification to the ISO/IEC 13660 mottle measurement algorithm that includes a band-pass, wavelet-based, filtering step to limit the contribution of high-frequency fluctuations including those introduced by print grain artifacts. This modification has improved the algorithm’s correlation with the subjective evaluation of experts who rated the severity of printed mottle artifacts. Seeking to improve upon the mottle algorithm in ISO/IEC 13660, the ISO 24790 committee evaluated several mottle metrics. This led to the selection of the above wavelet-based approach as the top candidate algorithm for inclusion in a future ISO/IEC standard. Recent experimental results from the ISO committee showed higher correlation between the wavelet-based approach and the subjective evaluation conducted by the ISO committee members based upon 25 samples covering a variety of printed mottle artifacts. In addition, we introduce an alternative approach for measuring mottle defects based on spatial frequency analysis of wavelet- filtered images. Our goal is to establish a link between the spatial-based mottle (ISO/IEC DTS 24790) approach and its equivalent frequency-based one in light of Parseval’s theorem. Our experimental results showed a high correlation between the spatial and frequency based approaches.

In the current market, reduction of warranty costs is an important avenue for improving profitability by manufacturers of printer products. 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. If the scanner unit of the MFP is not performing according to specification, this issue needs to be diagnosed. If there is a print quality issue, this can be diagnosed by printing a special test page that is resident in the firmware of the MFP unit, and then scanning it. However, the reliability of this process will be compromised if the scanner unit is defective. Thus, for both scanner and printer image quality issues, it is important to be able to properly evaluate the scanner performance. In this paper, we consider evaluation of the scanner performance by measuring its modulation transfer function (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. It is unacceptably expensive to ship every MFP with such a standard target, and to expect that the customer can keep track of it. To reduce this cost, in this paper, we develop new approach to this task. It is based on a self-printed slanted-edge target. Then, we propose algorithms to improve the results using a self-printed slanted-edge target. Finally, we present experimental results for MTF measurement using self-printed targets and compare them to the results obtained with standard targets.

The paper is devoted to a novel high-performance algorithm for automatic segmentation and skew correction of several objects on a scanned image. The complex multi-stage technique includes preprocessing, initial segmentation, classification of connected regions, merging of fragmented regions by heuristic procedure, bounding boxes detection and deskew of rectangular objects. Our method is highly effective owing to unification most of operations in one pass. Algorithm provides users with additional functionality and comfort. The method is evaluated by suggested quantitative quality criteria.

This paper introduces a homogeneity assessment method for the printed versions of uniform color images. This parameter has been specifically selected as one of the relevant attributes of printing quality. The method relies on image processing algorithms from a scanned image of the printed surface, especially the computation of gray level co-occurrence matrices and of objective homogeneity attribute inspired of Haralick's parameters. The viewing distance is also taken into account when computing the homogeneity index. Resizing and filtering of the scanned image are performed in order to keep the level of details visible by a standard human observer at short and long distances. The combination of the obtained homogeneity scores on both high and low resolution images provides a homogeneity index, which can be computed for any printed version of a uniform digital image. We tested the method on several hardcopies of a same image, and compared the scores to the empirical evaluations carried out by non-expert observers who were asked to sort the samples and to place them on a metric scale. Our experiments show a good matching between the sorting by the observers and the score computed by our algorithm.

Masking is a perceptual effect in which contents of the image reduce the ability of the observer to see the target signals hidden in the image. Characterization of masking effects plays an important role in modern image quality assessment (IQA) algorithms. In this work, we attribute the reduced sensitivity to the inhibition imposed by adjacent visual channels. In our model, each visual channel is excited by the contrast difference between the reference and distorted image in the corresponding channel and suppressed by the activities of the mask in adjacent channels. The model parameters are fitted to the results of a psychophysical experiment conducted with a set of different natural texture masks. Cross-validation is performed to demonstrate the model's performance in predicting the target detection threshold. The results of this work could be applied to improve the performance of current HVS-based IQA algorithms.

To achieve high perceptual quality of compressed images, many objective image quality metrics for compression artifacts evaluation and reduction have been developed based on characterization of local image features. However, it is the end user who is judging the image quality in various applications, so the validation of how well these metrics predict human perception is important and necessary. In this paper, we present a preliminary psychophysics experiment method to capture human perception of local ringing artifacts in JPEG images with different severity levels. Observers are asked to annotate the compressed image where they perceive artifacts along the edges, directly on the screen using an interactive tablet display. They are asked to catalog the severity of artifacts into one of the three levels: Strong, Medium, and Light. We process the hand-marked data into a ringing visibility edge map showing a ringing severity mean opinion score (MOS) at every edge pixel. The perceptual information captured in this experiment, enables us to study the correlation between human perception and local image features, which is an important step towards the goal of developing a non-reference (NR) objective metric to predict the visibility of JPEG ringing artifacts in alignment with the assessments of human observers.

When a mobile phone camera is tested and benchmarked, the significance of quality metrics is widely acknowledged. There are also existing methods to evaluate the camera speed. For example, ISO 15781 defines several measurements to evaluate various camera system delays. However, the speed or rapidity metrics of the mobile phone’s camera system have not been used with the quality metrics even if the camera speed has become more and more important camera performance feature. There are several tasks in this work. Firstly, the most important image quality metrics are collected from the standards and papers. Secondly, the speed related metrics of a mobile phone’s camera system are collected from the standards and papers and also novel speed metrics are identified. Thirdly, combinations of the quality and speed metrics are validated using mobile phones in the market. The measurements are done towards application programming interface of different operating system. Finally, the results are evaluated and conclusions are made. The result of this work gives detailed benchmarking results of mobile phone camera systems in the market. The paper defines also a proposal of combined benchmarking metrics, which includes both quality and speed parameters.

This paper presents a novel device and algorithms for measuring the different timings of digital cameras shooting both still images and videos. These timings include exposure (or shutter) time, electronic rolling shutter (ERS), frame rate, vertical blanking, time lags, missing frames, and duplicated frames. The device, the DxO LED Universal Timer (or “timer”), is designed to allow remotely-controlled automated timing measurements using five synchronized lines of one hundred LEDs each to provide accurate results; each line can be independently controlled if needed. The device meets the requirements of ISO 15781^[1]. Camera timings are measured by automatically counting the number of lit LEDs on each line in still and video images of the device and finding the positions of the LEDs within a single frame or between different frames. Measurement algorithms are completely automated: positional markers on the device facilitate automatic detection of the timer as well as the positions of lit LEDs in the images. No manual computation or positioning is required. We used this system to measure the timings of several smartphones under different lighting and setting parameters.

Since image processing aimed at reducing image noise can also remove important texture, standard methods for evaluating the capture and retention of image texture are currently being developed. Concurrently, the evolution of the intelligence and performance of camera noise-reduction (NR) algorithms poses a challenge for these protocols. Many NR algorithms are ‘content-aware’, which can lead to different levels of NR being applied to various regions within the same digital image. We review the requirements for improved texture measurement. The challenge is to evaluate image signal (texture) content without having a test signal interfere with the processing of the natural scene. We describe an approach to texture reproduction analysis that uses embedded periodic test signals within image texture regions. We describe a target that uses natural image texture combined with a multi-frequency periodic signal. This low-amplitude signal region is embedded in the texture image. Two approaches for embedding periodic test signals in image texture are described. The stacked sine-wave method uses a single combined, or stacked, region with several frequency components. The second method uses a low-amplitude version of the IEC-61146-1 sine-wave multi-burst chart, combined with image texture. A 3x3 grid of smaller regions, each with a single frequency, constitutes the test target. Both methods were evaluated using a simulated digital camera capture-path that included detector noise and optical MTF, for a range of camera exposure/ISO settings. Two types of image texture were used with the method, natural grass and a computed ‘dead-leaves’ region composed of random circles. The embedded-signal methods tested for accuracy with respect to image noise over a wide range of levels, and then further in an evaluation of an adaptive noise-reduction image processing.

Monitoring and controlling the quality of the viewing experience of videos transmitted over increasingly congested networks (especially wireless networks) is a pressing problem owing to rapid advances in video-centric mobile communication and display devices that are straining the capacity of the network infrastructure. New developments in automatic perceptual video quality models offer tools that have the potential to be used to perceptually optimize wireless video, leading to more efficient video data delivery and better received quality. In this talk I will review key perceptual principles that are, or could be used to create effective video quality prediction models, and leading quality prediction models that utilize these principles. The goal is to be able to monitor and perceptually optimize video networks by making them “quality-aware.”

During the last decade, the important advances and widespread availability of mobile technology (operating systems, GPUs, terminal resolution and so on) have encouraged a fast development of voice and video services like video-calling. While multimedia services have largely grown on mobile devices, the generated increase of data consumption is leading to the saturation of mobile networks. In order to provide data with high bit-rates and maintain performance as close as possible to traditional networks, the 3GPP (The 3rd Generation Partnership Project) worked on a high performance standard for mobile called Long Term Evolution (LTE). In this paper, we aim at expressing recommendations related to audio and video media profiles (selection of audio and video codecs, bit-rates, frame-rates, audio and video formats) for a typical video-calling services held over LTE/4G mobile networks. These profiles are defined according to targeted devices (smartphones, tablets), so as to ensure the best possible quality of experience (QoE). Obtained results indicate that for a CIF format (352 x 288 pixels) which is usually used for smartphones, the VP8 codec provides a better image quality than the H.264 codec for low bitrates (from 128 to 384 kbps). However sequences with high motion, H.264 in slow mode is preferred. Regarding audio, better results are globally achieved using wideband codecs offering good quality except for opus codec (at 12.2 kbps).

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients’ behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

This paper presents the results of a study of the impact of network transmission channel parameters on the quality of streaming video data. A common practice for estimating the interpretability of video information is to use the Motion Imagery Quality Equation (MIQE). MIQE combines a few technical features of video images (such as: ground sampling distance, relative edge response, modulation transfer function, gain and signal-to-noise ratio) to estimate the interpretability level. One observation of this study is that the MIQE does not fully account for video-specific parameters such as spatial and temporal encoding, which are relevant to appreciating degradations caused by the streaming process. In streaming applications the main artifacts impacting the interpretability level are related to distortions in the image caused by lossy decompression of video data (due to loss of information and in some cases lossy re-encoding by the streaming server). One parameter in MIQE that is influenced by network transmission errors is the Relative Edge Response (RER). The automated calculation of RER includes the selection of the best edge in the frame, which in case of network errors may be incorrectly associated with a blocked region (e.g. low resolution areas caused by loss of information). A solution is discussed in this document to address this inconsistency by removing corrupted regions from the image analysis process. Furthermore, a recommendation is made on how to account for network impairments in the MIQE, such that a more realistic interpretability level is estimated in case of streaming applications.

While objective and subjective quality assessment of images and video have been an active research topic in the recent years, multimedia technologies require new quality metrics and methodologies taking into account the fundamental differences in the human visual perception and the typical distortions of both video and audio modalities. Because of the importance of faces and especially the talking faces in the video sequences, this paper presents an audiovisual database that contains a different talking scenario. In addition to the video, the database also provides subjective quality scores obtained using a tailored single-stimulus test method (ACR). The resulting mean opinion scores (MOS) can be used to evaluate the performance of audiovisual quality metrics as well as for the comparison and for the design of new models.

Although a variety of successful no-reference (blind) picture quality analyzers have been proposed, progress on the blind video quality analysis problem has been slow. We break down the problem of perceptual blind video quality assessment (VQA) into components, which we address individually, before proposing a holistic solution. The idea is to tackle the challenges that comprise the blind VQA problem individually in order to gain a better understanding of it. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.

A recent development in the area of image and video quality consists of trying to incorporate aspects of visual attention in the design of visual quality metrics, mostly using the assumption that visual distortions appearing in less salient areas might be less visible and, therefore, less annoying. This research area is still in its infancy and results obtained by different groups are not yet conclusive. Among the works that have reported some improvement, most use subjective saliency maps, i.e. saliency maps generated from eye-tracking data obtained experimentally. Besides, most works address the image quality problem, not focusing on how to incorporate visual attention into video signals. In this work, we investigate the benefits of incorporating saliency maps obtained with visual attention. In particular, we compare the performance of four full-reference video quality metrics with their modified versions, which had saliency maps incorporated to the algorithm. For comparison proposes, we have used a database of subjective salience maps.

In this paper, we describe three psychophysical experiments with the goal of understanding the influence of audio and video components on the overall perceived audio- visual quality. In Experiment I, subjects evaluated the quality of videos (without any audio) compressed at different video bitrates. In Experiment II, subjects evaluated the quality of audio (without any video) compressed at different audio bitrates. In Experiment III, subjects evaluated the quality of videos (audio- visual signals), which had their audio and video components compressed at different bitrates. The results of these experiments show that compressing only the video have a higher impact on the overall perceived quality than compressing only the audio. Another important goal of this paper is to propose an objective model for the audio-visual quality. Using the gathered data from Experiments I, II, and III, we are able to obtain two models with reasonably good correlations with the overall perceived quality.

The paper presents metrics to estimate a number of spatial and temporal parameters relevant for stereoscopic 3D video content. Based mainly on view differences and disparity, the aim of these metrics is to check for common issues with 3D content that might make viewers uncomfortable. The algorithms are designed for high computational efficiency to permit real-time video content analysis, and are shown to be robust to common video impairments.

Gamma rays camera is widely used in many studies, including the image diagnostics of the radiation sources, flash photography, and nondestructive assessment (NDA), etc. As a major component of the high sensitivity gamma rays camera, the MCP image intensifier is characterized in the intensified image, tunable shutter time and gain. The gamma rays camera is consisting with rays-fluorescence convertor, the optical imaging system, the MCP image intensifier, CCD and other devices. The performance of the gamma rays camera is mainly dependent on such parameters as the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE), etc. All of the parameters are somewhat limited by the noise characteristics of the system. Compared with the standard derivative noise distribution, the NPS, which can reflect the evolution characteristics of the noise of the imaging system with the change of the spatial frequency, could convey more information on the noise distribution in the system. In this paper, theoretical analysis is presented on the major sources of the noise in the gamma rays camera. Based on the analysis, the noise power spectra of the gamma rays camera were calibrated under various radiation dosages respectively with the visible light and gamma rays radiation sources (0.2MeV and 1.25MeV in energy, respectively). As indicated by the experimental results, the noise is majorly induced by the fluctuations of the gain of the MCP image intensifier. And the remarkable noise peak occurs nearby the spatial frequency of about 0.633 Hz/mm. And almost the same phenomena were found with both the 0.2MeV and 1.25MeV radiation energy. Besides, the noise power spectra are in circular symmetrical distribution, whose intensities are rapidly decreased with the increasing spatial frequencies.

The wavefront passed through the atmosphere will produce different degree of distortion, due to atmospheric disturbances, defocus, aberration and etc. Distortion of wavefront can result in image degradation. Conventional methods typically use adaptive optics to correct the degradation. Correction system is complex and requires three parts, including wavefront detection, wavefront reconstruction, wavefront correction, and each part requires very precise control. In order to simplify the system structure, we use Hartmann - Shack wavefront sensor to get wavefront information, and then reconstruct the degenerated image using software restoration method. The paper introduces the background and significance of Hartmann-Shack wavefront sensor, summarizes the wavefront reconstruction principle. Then we analyze the general model of optical transfer function (OTF) and the way to calculate the OTF of diffraction limited incoherent image system. Take the actual situation into consideration, wavefront distortion is unavoidable, so we deduce the method to calculate OTF with wavefront distortion. Based on different wavefront detection error and the image restoration quality, we concluded the allowed maximum detection error under different peak value of wavefront.

The aim of this work is to study image quality of both single and multiply distorted images. We address the case of images corrupted by Gaussian noise or JPEG compressed as single distortion cases and images corrupted by Gaussian noise and then JPEG compressed, as multiply distortion case. Subjective studies were conducted in two parts to obtain human judgments on the single and multiply distorted images. We study how these subjective data correlate with No Reference state-of-the-art quality metrics. We also investigate proper combining of No Reference metrics to achieve better performance. Results are analyzed and compared in terms of correlation coefficients.

This investigation examines the relationships between image fidelity, acceptability thresholds and scene content for images distorted by lossy compression. Scene characteristics of a sample set of images, with a wide range of representative scene content, were quantified, using simple measures (scene metrics), which had been previously found to correlate with global scene lightness, global contrast, busyness, and colorfulness. Images were compressed using the lossy JPEG 2000 algorithm to a range of compression ratios, progressively introducing distortion to levels beyond the threshold of detection. Twelve observers took part in a paired comparison experiment to evaluate the perceptibility threshold compression ratio. A further psychophysical experiment was conducted using the same scenes, compressed to higher compression ratios, to identify the level of compression at which the images became visually unacceptable. Perceptibility and acceptability thresholds were significantly correlated for the test image set; both thresholds also correlated with the busyness metric. Images were ranked for the two thresholds and were further grouped, based upon the relationships between perceptibility and acceptability. Scene content and the results from the scene descriptors were examined within the groups to determine the influence of specific common scene characteristics upon both thresholds.

Tiled displays fill the increasingly important need to display very large images. As these displays become more common, the ability to objectively measure their visual quality becomes more important. One cost of the size flexibility offered by these displays is the grid-type distortion created by the gaps between each sub-display's active area. General purpose Image Quality Assessment (IQA) metrics are commonly used to measure the visual quality effect of image distortions such as blur and white noise, but no research has been performed to determine their suitability for tiling distortions. This paper addresses that research gap by creating a new image quality database specifically targeting tiled images. Common state-of-the-art IQA metrics are tested against this new database and their performances are compared between tiled distortions and `traditional' image distortions.