Selecting stimuli parameters for video quality assessment studies based on perceptual similarity distances

Kumcu, Asli; Platiša, Ljiljana; Heng, Chen; Gislason‐Lee, Amber J.; Davies, Andrew G.; Schelkens, Peter; Taeymans, Yves; Philips, Wilfried

doi:10.1117/12.2083431

Cited by 3 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…47 Their results indicate that the obtained subjective scores were roughly well distributed over its entire range. Nevertheless, that methodology requires a small subjective pre-study (pilot study) for modeling the relationship between parameter levels and PVQ.…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 59%

“…20,47 Figure 11(c) shows an example in which the proposed methodology does not divide the DMOS domain equally, i.e., the points are not equally distributed over the whole perceived quality range. In any case, the plots show that the selected values using the proposed methodology (crosses) are more uniformly distributed in the DMOS axis than the ones selected in the original database (squares, triangles, and diamonds).…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

“…For instance, Fig. 10(a) shows an example of subjective quality scores distributed over the 20,47 (e.g., DMOS>0.9). On the other hand, in Figs.…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

“…This usefulness is reflected by whether or not the resulting DMOS scores are uniformly distributed over its entire range, which depends completely on the selected acquisition, processing and technical parameters. 47 It is known that such parameters (e.g., noise, blur, compression level, compression rate, PSNR value, among others) are often nonlinearly related to PVQ and the model of the relationship may be unknown a priori. Figure 10 shows plots of DMOS in function of PSNR for different cases of source content, taken from IRCCyN and CIF EPFL-PoliMI databases.…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

“…To address the problem of adequate parameter selection for the test stimuli, Kumcu et al 47 have proposed a method for modeling the relationship between parameter levels and PVQ using a paired comparison procedure in which subjects judge the perceived similarity in quality. 47 Their results indicate that the obtained subjective scores were roughly well distributed over its entire range.…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

See 4 more Smart Citations

Content-aware objective video quality assessment

Ortiz-Jaramillo

Niño-Castañeda

Platiša

et al. 2016

J. Electron. Imaging

Self Cite

View full text Add to dashboard Cite

Abstract. Since the end-user of video-based systems is often a human observer, prediction of user-perceived video quality (PVQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures (VQMs), their lack of generalizability remains a problem. This is mainly due to the strong dependency between PVQ and video content. Although this problem is well known, few existing VQMs directly account for the influence of video content on PVQ. Recently, we proposed a method to predict PVQ by introducing relevant video content features in the computation of video distortion measures. The method is based on analyzing the level of spatiotemporal activity in the video and using those as parameters of the anthropomorphic video distortion models. We focus on the experimental evaluation of the proposed methodology based on a total of five public databases, four different objective VQMs, and 105 content related indexes. Additionally, relying on the proposed method, we introduce an approach for selecting the levels of video distortions for the purpose of subjective quality assessment studies. Our results suggest that when adequately combined with content related indexes, even very simple distortion measures (e.g., peak signal to noise ratio) are able to achieve high performance, i.e., high correlation between the VQM and the PVQ. In particular, we have found that by incorporating video content features, it is possible to increase the performance of the VQM by up to 20% relative to its noncontent-aware baseline.

show abstract

Section: Selecting Test Sequences For Subjectivementioning

confidence: 59%

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

“…For instance, Fig. 10(a) shows an example of subjective quality scores distributed over the 20,47 (e.g., DMOS>0.9). On the other hand, in Figs.…”

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

Section: Selecting Test Sequences For Subjectivementioning

confidence: 99%

See 3 more Smart Citations

Content-aware objective video quality assessment

Ortiz-Jaramillo

Niño-Castañeda

Platiša

et al. 2016

J. Electron. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

Influence of study design on digital pathology image quality evaluation: the need to define a clinical task

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. 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. © The Authors. Published by SPIE under aCreative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

A visibility overshoot index for interventional x-ray image quality assessment

Kumcu¹,

Platiša

Goossens

et al. 2023

Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

View full text Add to dashboard Cite

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.

show abstract

Selecting stimuli parameters for video quality assessment studies based on perceptual similarity distances

Cited by 3 publications

References 12 publications

Content-aware objective video quality assessment

Content-aware objective video quality assessment

Influence of study design on digital pathology image quality evaluation: the need to define a clinical task

A visibility overshoot index for interventional x-ray image quality assessment

Contact Info

Product

Resources

About