Predicting the Quality of Images Compressed after Distortion in Two Steps.

In a typical communication pipeline, images undergo a series of processing steps that can cause visual distortions before being viewed. Given a high quality reference image, a reference (R) image quality assessment (IQA) algorithm can be applied after compression or transmission. However, the assumption of a high quality reference image is often not fulfilled in practice, thus contributing to less accurate quality predictions when using stand-alone R IQA models. This is particularly common on social media, where hundreds of billions of usergenerated photos and videos containing diverse, mixed distortions are uploaded, compressed, and shared annually on sites like Facebook, YouTube, and Snapchat. The qualities of the pictures that are uploaded to these sites vary over a very wide range. While this is an extremely common situation, the problem of assessing the qualities of compressed images against their precompressed, but often severely distorted (reference) pictures has been little studied. Towards ameliorating this problem, we propose a novel two-step image quality prediction concept that combines NR with R quality measurements. Applying a first stage of NR IQA to determine the possibly degraded quality of the source image yields information that can be used to quality-modulate the R prediction to improve its accuracy. We devise a simple and efficient weighted product model of R and NR stages, which combines a pre-compression NR measurement with a post-compression R measurement. This first-of-a-kind two-step approach produces more reliable objective prediction scores. We also constructed a new, first-of-a-kind dedicated database specialized for the design and testing of two-step IQA models. Using this new resource, we show that twostep approaches yield outstanding performance when applied to compressed images whose original, pre-compression quality covers a wide range of realistic distortion types and severities. The two-step concept is versatile as it can use any desired R and NR components. We are making the source code of a particularly efficient model that we call 2stepQA publicly available at https://github.com/xiangxuyu/2stepQA. We are also providing the dedicated new two-step database free of charge at http://live.ece.utexas.edu/research/twostep/index.html.

Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality.

Developing methods to predict how image quality affects the task performance is a topic of great interest in many applications. While such studies have been performed in the medical imaging community, little work has been reported in the security X-ray imaging literature. In this paper, we develop models that predict the effect of image quality on the detection of the improvised explosive device components by bomb technicians in images taken using portable X-ray systems. Using a newly developed NIST-LIVE X-Ray Task Performance Database, we created a set of objective algorithms that predict bomb technician detection performance based on the measures of image quality. Our basic measures are traditional image quality indicators (IQIs) and perceptually relevant natural scene statistics (NSS)-based measures that have been extensively used in visible light image quality prediction algorithms. We show that these measures are able to quantify the perceptual severity of degradations and can predict the performance of expert bomb technicians in identifying threats. Combining NSS- and IQI-based measures yields even better task performance prediction than either of these methods independently. We also developed a new suite of statistical task prediction models that we refer to as quality inspectors of X-ray images (QUIX); we believe this is the first NSS-based model for security X-ray images. We also show that QUIX can be used to reliably predict conventional IQI metric values on the distorted X-ray images.

Modeling the Perceptual Quality of Immersive Images Rendered on Head Mounted Displays: Resolution and Compression.

We develop a model that expresses the joint impact of spatial resolution s and JPEG compression quality factor qf on immersive image quality. The model is expressed as the product of optimized exponential functions of these factors. The model is tested on a subjective database of immersive image contents rendered on a head mounted display (HMD). High Pearson correlation and Spearman correlation (> 0.95) and small relative root mean squared error (< 5.6%) are achieved between the model predictions and the subjective quality judgements. The immersive ground-truth images along with the rest of the database are made available for future research and comparisons.

Multivariate Statistical Approach to Image Quality Tasks.

Many existing Natural Scene Statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher-order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, that facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.

RBx10080307, a dual EGFR/IGF-1R inhibitor for anticancer therapy.

Pharmacological intervention of epidermal growth factor receptor (EGFR) family members by antibodies or small molecule inhibitors has been one of the most successful approaches for anticancer therapy. However this therapy has its own limitations due to the development of resistance, over a period of time. One of the possible causes of the development of resistance to the therapy with EGFR inhibitors could be the simultaneous activation of parallel pathways. Both EGFR and insulin like growth factor-1 receptor (IGF-1R) pathways are reported to act reciprocal to each other and converge into the mitogen activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) pathways. Inhibiting one pathway alone may therefore not be sufficient and could be a cause of development of resistance. The other cause could be mutations of EGFR which would be less sensitive to the inhibitors. We, therefore, suggest that co-targeting IGF-1R and EGFR kinases by dual inhibitors can lead to improved efficacy and address the problems of resistance. In the present manuscript, we report the identification of a novel, small molecule dual EGFR/IGF-1R inhibitor, RBx10080307 which displayed in vitro activity at the molecular level and oral efficacy in mouse xenograft model. The compound also showed in vitro activity in an EGFR mutant cell line and may thus have the potential to show activity in resistant conditions. Additional efficacy studies are needed in EGFR resistant mouse cancer model and if found efficacious, this can be a major advantage over standalone erlotinib and other existing therapies.

WITHDRAWN: Derivatives of cis-1,2,3,6-tetrahydrophthalimide for the treatment of benign prostatic hyperplasia.

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