Software package for measurement of quality indicators working in no-reference model

Nawała, Jakub; Leszczuk, Mikołaj; Zajdel, Michał; Baran, Remigiusz

doi:10.1007/s11042-016-4195-3

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…Performance-wise, the execution of the algorithm can be divided into two parts (i) computation of the indicators and (ii) execution of the XGB Classifier. The performance of the indicator calculation is described in detail in [ 16 ]. Execution of all indicators on a single 1920 × 1080 frame in a single-threaded application on an Intel Core i7-3537U CPU takes 0.122 s. This time can be further reduced to 0.089 s by using multithreading.…”

Section: Resultsmentioning

confidence: 99%

“…The concept presented here is known as artifacts (or KPIs), which are broken down into four groups based on where they came from: a category for capturing processing, transmitting, and displaying. In addition to helping to set the algorithm, extend the monitoring intervals, and ensure improved QoE prediction, the program can isolate and improve event investigation [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

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“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition

Leszczuk

Kobosko

Nawała

et al. 2023

Sensors

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In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to “in the wild” as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective “in the wild” video content recognition model. The final model is trained and tested using video sequence databases with professional content and “in the wild” content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip).

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition

Leszczuk

Kobosko

Nawała

et al. 2023

Sensors

Self Cite

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“…Blockiness [2,21]: Refers to the visual artifacts caused by block-based coding schemes, often seen as grid-like structures in the image or video; 3.…”

Section: Indicatorsmentioning

confidence: 99%

Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition

Leszczuk,

Janowski,

Nawała

et al. 2023

Electronics

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In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this domain, this study introduces a novel approach tailored to Automatic License Plate Recognition (ALPR). We developed a robust evaluation framework using a dataset with ground truth coordinates for ALPR. This dataset includes video frames captured under various conditions, including occlusions, to facilitate comprehensive model training, testing, and validation. Our methodology simulates quality degradation using a digital camera image acquisition model, representing how luminous flux is transformed into digital images. The model’s performance was evaluated using Video Quality Indicators within an OpenALPR library context. Our findings show that the model achieves a high F-measure score of 0.777, reflecting its effectiveness in assessing video quality for recognition tasks. The proposed model presents a promising avenue for accurate video quality assessment in ALPR tasks, outperforming traditional metrics in typical recognition application scenarios. This underscores the potential of the methodology for broader adoption in video quality analysis for recognition purposes.

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“…This allows it to operate in a distortion-generic fashion. This is in contrast to other solutions that provide distortion-specific quality indication [2]. Importantly, one of the newest trends is to use the existing neural network architectures (dedicated for object detection) and, building upon them, propose better performing NR IQA solutions [3].…”

Section: Introductionmentioning

confidence: 99%

“…The only video data set that contains audio. It is present in 97% of stimuli 2. One more, custom scoring method is utilised.…”

mentioning

confidence: 99%

Study of Subjective Data Integrity for Image Quality Data Sets with Consumer Camera Content

et al. 2020

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We need data sets of images and subjective scores to develop robust no reference (or blind) visual quality metrics for consumer applications. These applications have many uncontrolled variables because the camera creates the original media and the impairment simultaneously. We do not fully understand how this impacts the integrity of our subjective data. We put forward two new data sets of images from consumer cameras. The first data set, CCRIQ2, uses a strict experiment design, more suitable for camera performance evaluation. The second data set, VIME1, uses a loose experiment design that resembles the behavior of consumer photographers. We gather subjective scores through a subjective experiment with 24 participants using the Absolute Category Rating method. We make these two new data sets available royalty-free on the Consumer Digital Video Library. We also present their integrity analysis (proposing one new approach) and explore the possibility of combining CCRIQ2 with its legacy counterpart. We conclude that the loose experiment design yields unreliable data, despite adhering to international recommendations. This suggests that the classical subjective study design may not be suitable for studies using consumer content. Finally, we show that Hoßfeld–Schatz–Egger α failed to detect important differences between the two data sets.

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Software package for measurement of quality indicators working in no-reference model

Cited by 6 publications

References 14 publications

“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition

“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition

Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition

Study of Subjective Data Integrity for Image Quality Data Sets with Consumer Camera Content

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