8 results on '"G. Martini"'
Search Results
2. Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment
- Author
-
Maria G. Martini, Aditya Raj, Anil Kumar Tiwari, and Nisarg Shah
- Subjects
Multivariate statistics ,General Computer Science ,Computer science ,media_common.quotation_subject ,convolutional neural network ,02 engineering and technology ,Fundus (eye) ,Convolutional neural network ,Field (computer science) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,General Materials Science ,Quality (business) ,Fundus image quality assessment ,Block (data storage) ,media_common ,business.industry ,General Engineering ,Pattern recognition ,Diabetic retinopathy ,medicine.disease ,diabetic retinopathy ,020201 artificial intelligence & image processing ,multivariate regression ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 - Abstract
Objectively assessing the perceptual quality of an ocular fundus image is essential for the reliable diagnosis of various ocular diseases. A fair amount of work has been done in this field to date. However, the generalizability of the current work is limited, as the existing quality models were developed and evaluated with data-sets built with limited subjective inputs. This paper aims at addressing this limitation with the following two contributions. First, a new fundus image quality assessment (FIQuA) data-set is presented, containing 1500 fundus images with three classes of quality: Good, Fair, and Poor. Also, for each image, subjective scores (in the range [0-10]) were collected for six quality parameters, including structural and generic properties of the fundus images. Second, a new multivariate regression based convolutional neural network (CNN) model is proposed to predict the fundus image quality. The proposed model consists of two individually trained blocks. The first block consists of four pre-trained models, trained against the subjective scores for the six quality parameters, and aims at deriving the optimized features for classification. Next, the optimized features from each of the four models are ensembled together and transferred to the second block for final classification. The proposed model achieves a strong correlation with the subjective scores, with the values 0.941 , 0.954 , 0.853 , and 0.401 obtained for SROCC, LCC, KCC, and RMSE respectively. Its classification accuracy is 95.66% over the FIQuA data-set, and 98.96% and 88.43% respectively over the two publicly available data-sets DRIMDB and EyeQ.
- Published
- 2020
3. Lossless Compression of Data From Static and Mobile Dynamic Vision Sensors-Performance and Trade-Offs
- Author
-
Maria G. Martini, Khurram Iqbal, and Nabeel Khan
- Subjects
Lossless compression ,General Computer Science ,Computer science ,020208 electrical & electronic engineering ,Real-time computing ,Trade offs ,General Engineering ,020207 software engineering ,02 engineering and technology ,Data_CODINGANDINFORMATIONTHEORY ,Dynamic vision senor ,neuromorphic computing ,spike coding ,computer vision ,Neuromorphic engineering ,Wide dynamic range ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,dictionary based compression ,lcsh:TK1-9971 ,data compression ,Data compression - Abstract
Dynamic Vision Sensors (DVS) are emerging retinomorphic visual capturing devices, with great advantages over conventional vision sensors in terms of wide dynamic range, low power consumption, and high temporal resolution. The bio-inspired approach of the DVS results in lower data rates than conventional vision sensors. Still, such data can be further compressed. Compression of DVS data is an emerging research area and a detailed performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed and latency. Moreover, the compression performance analysis is performed under diverse scenarios including stationary and mobile DVS. According to the detailed experimental analysis, Lempel-Ziv-Markov chain algorithm (LZMA) achieves the best compression ratios among all the considered strategies for the case when the DVS is static. On the other hand, Spike coding achieves the best compression ratios under the scenario when spike events are produced by a sensor in motion. However, both strategies result in low compression speed and high latency which restrict the applications of these strategies in real-time scenarios. The Brotli strategy achieves the best trade-off between compression ratio, speed and latency under static as well as mobile scenarios. We also observe a significant decrease in compression and decompression performance (in terms of ratio, speed and latency) of all the strategies under mobile DVS scenarios.
- Published
- 2020
4. QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges
- Author
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Nabajeet Barman and Maria G. Martini
- Subjects
video quality assessment ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,TCP ,lcsh:TK1-9971 ,QoE modeling - Abstract
With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.
- Published
- 2019
5. Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges
- Author
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Chaminda T. E. R. Hewage, Arslan Ahmad, Thanuja Mallikarachchi, Nabajeet Barman, and Maria G. Martini
- Subjects
Time-varying video quality ,Quality of Experience (QoE) ,quality models ,video streaming ,continuous time-varying quality ,video compression ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The multimedia delivery chain consists of multiple stages such as content preparation, content delivery via Over-The-Top delivery network and Internet Service Providers network. Within the multimedia service chain, each stage influences the Quality of Experience (QoE) of the end user. The objective of this work is to provide a comprehensive literature survey with future research challenges and opportunities in the field of time-varying video quality in multimedia service delivery. The contribution of this work is two fold: 1) Survey – we provide a review of state-of-the-art works for video quality models to quantify multiple artifacts into a single QoE metric, pooling strategies for global quality measurements, and Continuous Time-Varying Quality (CTVQ) models; 2) Future Challenges and Directions – we investigate ten major research challenges and future directions based on the state-of-the-art for QoE modelling, QoE-aware encoding/decoding and QoE monitoring/management of multimedia streaming in next-generation networks.
- Published
- 2022
- Full Text
- View/download PDF
6. Lossless Compression of Data From Static and Mobile Dynamic Vision Sensors-Performance and Trade-Offs
- Author
-
Nabeel Khan, Khurram Iqbal, and Maria G. Martini
- Subjects
Dynamic vision senor ,neuromorphic computing ,computer vision ,spike coding ,data compression ,dictionary based compression ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Dynamic Vision Sensors (DVS) are emerging retinomorphic visual capturing devices, with great advantages over conventional vision sensors in terms of wide dynamic range, low power consumption, and high temporal resolution. The bio-inspired approach of the DVS results in lower data rates than conventional vision sensors. Still, such data can be further compressed. Compression of DVS data is an emerging research area and a detailed performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed and latency. Moreover, the compression performance analysis is performed under diverse scenarios including stationary and mobile DVS. According to the detailed experimental analysis, Lempel-Ziv-Markov chain algorithm (LZMA) achieves the best compression ratios among all the considered strategies for the case when the DVS is static. On the other hand, Spike coding achieves the best compression ratios under the scenario when spike events are produced by a sensor in motion. However, both strategies result in low compression speed and high latency which restrict the applications of these strategies in real-time scenarios. The Brotli strategy achieves the best trade-off between compression ratio, speed and latency under static as well as mobile scenarios. We also observe a significant decrease in compression and decompression performance (in terms of ratio, speed and latency) of all the strategies under mobile DVS scenarios.
- Published
- 2020
- Full Text
- View/download PDF
7. Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment
- Author
-
Aditya Raj, Nisarg A. Shah, Anil Kumar Tiwari, and Maria G. Martini
- Subjects
Fundus image quality assessment ,diabetic retinopathy ,multivariate regression ,convolutional neural network ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Objectively assessing the perceptual quality of an ocular fundus image is essential for the reliable diagnosis of various ocular diseases. A fair amount of work has been done in this field to date. However, the generalizability of the current work is limited, as the existing quality models were developed and evaluated with data-sets built with limited subjective inputs. This paper aims at addressing this limitation with the following two contributions. First, a new fundus image quality assessment (FIQuA) data-set is presented, containing 1500 fundus images with three classes of quality: Good, Fair, and Poor. Also, for each image, subjective scores (in the range [0-10]) were collected for six quality parameters, including structural and generic properties of the fundus images. Second, a new multivariate regression based convolutional neural network (CNN) model is proposed to predict the fundus image quality. The proposed model consists of two individually trained blocks. The first block consists of four pre-trained models, trained against the subjective scores for the six quality parameters, and aims at deriving the optimized features for classification. Next, the optimized features from each of the four models are ensembled together and transferred to the second block for final classification. The proposed model achieves a strong correlation with the subjective scores, with the values 0.941, 0.954, 0.853, and 0.401 obtained for SROCC, LCC, KCC, and RMSE respectively. Its classification accuracy is 95.66% over the FIQuA data-set, and 98.96% and 88.43% respectively over the two publicly available data-sets DRIMDB and EyeQ.
- Published
- 2020
- Full Text
- View/download PDF
8. No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications
- Author
-
Nabajeet Barman, Emmanuel Jammeh, Seyed Ali Ghorashi, and Maria G. Martini
- Subjects
Quality assessment ,no reference ,gaming video streaming ,machine learning ,regression ,quality of experience ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.
- Published
- 2019
- Full Text
- View/download PDF
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