Asli Kumcu

Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly higher brightness and, therefore, contrast levels than today’s models will become increasingly accessible and affordable to consumers. While HDR technology has gained prominence over the past few years, low dynamic range (LDR) content is still consumed due to a substantial volume of historical multimedia content being recorded and preserved in LDR. Although the amount of HDR content will continue to increase as HDR becomes more prevalent, a large portion of multimedia content currently remains in LDR. In addition, it is worth noting that although the HDR standard supports multimedia content with luminance levels up to 10,000 cd/m² (a standard measure of brightness), most HDR content is typically limited to a maximum brightness of around 1000 cd/m². This limitation aligns with the current capabilities of consumer HDR TVs but is a factor approximately five times brighter than current LDR TVs. To accurately present LDR content on a HDR display, it is processed through a dynamic range expansion process known as inverse tone mapping (iTM). This LDR to HDR conversion faces many challenges, including the inducement of noise artifacts, false contours, loss of details, desaturated colors, and temporal inconsistencies. This paper introduces complete inverse tone mapping, artifact suppression, and a highlight enhancement pipeline for video sequences designed to address these challenges. Our LDR-to-HDR technique is capable of adapting to the peak brightness of different displays, creating HDR video sequences with a peak luminance of up to 6000 cd/m². Furthermore, this paper presents the results of comprehensive objective and subjective experiments to evaluate the effectiveness of the proposed pipeline, focusing on two primary aspects: real-time operation capability and the quality of the HDR video output. Our findings indicate that our pipeline enables real-time processing of Full HD (FHD) video (1920 × 1080 pixels), even on hardware that has not been optimized for this task. Furthermore, we found that when applied to existing HDR content, typically capped at a brightness of 1000 cd/m², our pipeline notably enhances its perceived quality when displayed on a screen that can reach higher peak luminances.

In recent years, radar has become a crucial part of road scene perception. In particular, radar increases sensing reliability in poor weather and lighting conditions. State-of-the-art deep learning methods require training, but the labeling of radar data needed to generate the required “ground truth” is time-consuming and requires expert knowledge, due to the confusing nature of radar signals. This limits the development of accurate radar models. To address the difficulty of annotating radar datasets, we propose a novel semi-supervised learning framework we call Automotive Radar Consistency (ARC), which enables the inclusion of unlabeled radar frames during training. This improves performance without adding manual labor. Our model learns object features based on micro-Doppler signatures from a time series of radar frames. We posit that the same object in both the forward and reverse temporal directions will share similar features, and we use this as a consistency regularization to encourage the model to learn how to distinguish targets. In addition, we propose Focal JS-divergence to alleviate over-fitting and address the class imbalance problem in semi-supervised learning, by focusing on the hard classes and samples in the unlabeled data. Our proposed method is a general training strategy that can be incorporated in most existing deep learning frameworks. Our experimental results on two public datasets with limited annotations show that our method significantly improves the performance of existing supervised methods. Under the same network architecture, our method outperforms the fully supervised method in object detection by as much as 36%.

Dose reduction remains an important goal in interventional x-ray. We propose an image quality (IQ) measure called the visibility overshoot index. Given a patient image and a specified clinical task, the index quantifies the maximum acceptable dose reduction. The dose control system can then use this information to deliver the minimum dose necessary for detection of clinical signals, reducing unnecessary radiation exposure. We developed an experimental visual model to estimate signal detectability as a function of image features such as noise and signal contrast. The model is used to find a feature’s threshold–the maximum change in noise or signal contrast where signal detectability remains possible. An automated algorithm measures the magnitudes of these features on a frame. Visibility overshoot is expressed in terms of the image features: the noise overshoot and contrast overshoot indices are the ratio of the threshold to measured noise/contrast. The indices demonstrate good agreement with detector dose, channelized hotelling observer results, and clinicians’ judgments. In our study of a cylindrical object phantom acquired at seven dose levels, we found that the noise overshoot index is linearly related to the square root of detector dose and the CHO detectability index, with Pearson correlation 0.995–1.0 for signals 1-4 mm diameter. For interventional cardiology and neurology sequences acquired at standard and 25–30% dose, the index and clinicians rank IQ similarly. Results on the phantom suggest at least 15% dose reduction could be achieved in fluoroscopy mode. Our patient-specific IQ approach could bring additional dose savings to clinical practice.

The editorial introduces the JMI Special Section on Medical Image Perception and Observer Performance.

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.

Despite the current rapid advance in technologies for whole slide imaging, there is still no scientific consensus on the recommended methodology for image quality assessment of digital pathology slides. For medical images in general, it has been recommended to assess image quality in terms of doctors’ success rates in performing a specific clinical task while using the images (clinical image quality, cIQ). However, digital pathology is a new modality, and already identifying the appropriate task is difficult. In an alternative common approach, humans are asked to do a simpler task such as rating overall image quality (perceived image quality, pIQ), but that involves the risk of nonclinically relevant findings due to an unknown relationship between the pIQ and cIQ. In this study, we explored three different experimental protocols: (1) conducting a clinical task (detecting inclusion bodies), (2) rating image similarity and preference, and (3) rating the overall image quality. Additionally, within protocol 1, overall quality ratings were also collected (task-aware pIQ). The experiments were done by diagnostic veterinary pathologists in the context of evaluating the quality of hematoxylin and eosin-stained digital pathology slides of animal tissue samples under several common image alterations: additive noise, blurring, change in gamma, change in color saturation, and JPG compression. While the size of our experiments was small and prevents drawing strong conclusions, the results suggest the need to define a clinical task. Importantly, the pIQ data collected under protocols 2 and 3 did not always rank the image alterations the same as their cIQ from protocol 1, warning against using conventional pIQ to predict cIQ. At the same time, there was a correlation between the cIQ and task-aware pIQ ratings from protocol 1, suggesting that the clinical experiment context (set by specifying the clinical task) may affect human visual attention and bring focus to their criteria of image quality. Further research is needed to assess whether and for which purposes (e.g., preclinical testing) task-aware pIQ ratings could substitute cIQ for a given clinical task.

Standardization bodies recommend various protocols to conduct subjective quality assessment (QA) of imaging systems. While many studies have compared these QA protocols, few have assessed the impact of different approaches for preprocessing and analyzing quality ratings. Furthermore, the effect of large versus small quality differences on the discrimination performance of protocols has not been extensively studied in video QA. This study examines these issues for four QA protocols. H.264 compressed medical videos and denoised natural scene videos were evaluated by expert and naive subjects. Scores were collected with four QA protocols-forced choice (FC), two ratio-scaled paired comparison methods: preference (Pref) and dissimilarity (Dissim), and single stimulus (SS)-and analyzed using combinations of different rating preprocessing approaches, generating a total of 14 methods. Performance metrics-probability and effect size-quantified the ability of the methods to discriminate between quality levels. The Pref and Dissim methods analyzed with classical multidimensional scaling and the SS method with Z-score transformation consistently outperformed the other methods. The type of preprocessing introduced large differences in the performance of individual protocols. Grouping stimuli pairs by small and large quality differences introduced significant differences in the performance rankings of the methods, with Pref and Dissim being most sensitive to small quality differences. For the future, we suggest further validation of the FC method, due to its simplicity and ease of use, and continued investigation into more robust raw scores transformation and statistical analysis methods for both SS and FC ratings.

Background: Few telesurgery studies assess the impact of latency on user experience, low latencies are often not studied despite evidence of negative effects, and some studies recruit inexperienced subjects instead of surgeons without evidence that latency affects both groups similarly.Methods: Fifteen trainees and fourteen laparoscopic surgeons conducted two tasks on a laparoscopy home‐trainer at six latencies below 200 milliseconds (ms). Completion time and usability (perceived awareness of latency, inefficiency, disturbance, adaptability, and impact on patient safety) were measured.Results: Weak correlation between completion time and usability was found. There was significant deterioration in performance and user experience at 105 ms added latency. Surgeons were more negatively affected.Conclusion: Objective measures insufficiently describe the impact of latency therefore standard measures of user experience should be incorporated in studies. Even low latencies may be detrimental to laparoscopic surgery. Results from non‐experts cannot predict the impact of latency on experienced surgeons. Copyright © 2016 John Wiley & Sons, Ltd.