Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu

Assessment of sexual difficulties associated with multi-modal treatment for cervical or endometrial cancer: A systematic review of measurement instruments

Background: Practitioners and researchers require an outcome measure that accurately identifies the range of common treatment-induced changes in sexual function and well-being experienced by women after cervical or endometrial cancer. This systematic review critically appraised the measurement properties and clinical utility of instruments validated for the measurement of female sexual dysfunction (FSD) in this clinical population. Methods: A bibliographic database search for questionnaire development or validation papers was completed and methodological quality and measurement properties of selected studies rated using the Consensus-based Standards for the selection of health Measurement Instrument (COSMIN) checklist. Results: 738 articles were screened, 13 articles retrieved for full text assessment and 7 studies excluded, resulting in evaluation of 6 papers; 2 QoL and 4 female sexual morbidity measures. Five of the six instruments omitted one or more dimension of female sexual function and only one instrument explicitly measured distress associated with sexual changes as per DSM V (APA 2013) diagnostic criteria. None of the papers reported measurement error, responsiveness data was available for only two instruments, three papers failed to report on criterion validity, and test-retest reliability reporting was inconsistent. Heterosexual penile-vaginal intercourse remains the dominant sexual activity focus for sexual morbidity PROMS terminology and instruments lack explicit reference to solo or non-coital sexual expression or validation in a non-heterosexual sample. Four out of six instruments included mediating treatment or illness items such as vaginal changes, menopause or altered body image. Conclusions: Findings suggest that the Female Sexual Function Index (FSFI) remains the most robust sexual morbidity outcome measure, for research or clinical use, in sexually active women treated for cervical or endometrial cancer

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Image quality assessment based on harmonics gain/loss information

We present an objective reduced-reference image quality assessment method based on harmonic gain/loss information through a discriminative analysis of local harmonic strength (LHS). The LHS is computed from the gradient of images, and its value represents a relative degree of the appearance of blockiness on images when it is related to energy gain within an image. Furthermore, comparison between local harmonic strength values from an original, distortion-free image and a degraded, processed, or compressed version of the image shows that the LHS can also be used to indicate other types of degradations, such as blurriness that corresponds with energy loss. Our simulations show that we can develop a single metric based on this gain/loss information and use it to rate the quality of images encoded by various encoders such as DCT-based JPEG, wavelet-based JPEG 2000, or various processed images. We show that our method can overcome some limitations of the traditional PSNR

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.Comment: Accepted to CVPR 2018; Code and data available at https://www.github.com/richzhang/PerceptualSimilarit

Semantic multimedia remote display for mobile thin clients

Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE