DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network

Pan, Zhaoqing; Zhang, Hao; Lei, Jianjun; Fang, Yuming; Shao, Xiao; Ling, Nam

doi:10.1109/tcsvt.2022.3188991

Cited by 41 publications

(12 citation statements)

References 48 publications

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“…Initially, shallow machine learning approaches depending on feature engineering were used to classify and predict issues. However, due to their numerous disadvantages, researchers have begun to focus on deep learning methods [ 34 ]. Deep learning has recently become popularly employed for sequence classification [ 35 ], biological information [ 36 ], image processing [ 37 ], computer vision [ 38 ], natural language processing [ 39 ], and other sectors, with positive outcomes.…”

Section: Cnn’s Model Setting and Phasesmentioning

confidence: 99%

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Hamdy

Ismail

Awad

et al. 2023

Sensors

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MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

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Section: Cnn’s Model Setting and Phasesmentioning

confidence: 99%

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Hamdy

Ismail

Awad

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Multiple such IQA approaches countenance this issue, but still, these methods have various issues, i.e., in order to predict the image quality, high-level semantic features are utilized. Furthermore, such approaches try to combine the effects of both types of distorted images by using a bilinear pooling operation, which is inadequate for capturing the relationship between these two types of distorted images [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Feature Fusion and Kernel-Based Regression Modeling to Improve Blind Image Quality Assessment

Ryu

2023

Applied Sciences

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In the fields of image processing and computer vision, evaluating blind image quality (BIQA) is still a difficult task. In this paper, a unique BIQA framework is presented that integrates feature extraction, feature selection, and regression using a support vector machine (SVM). Various image characteristics are included in the framework, such as wavelet transform, prewitt and gaussian, log and gaussian, and prewitt, sobel, and gaussian. An SVM regression model is trained using these features to predict the quality ratings of photographs. The proposed model uses the Information Gain attribute approach for feature selection to improve the performance of the regression model and decrease the size of the feature space. Three commonly used benchmark datasets, TID2013, CSIQ, and LIVE, are utilized to assess the performance of the proposed methodology. The study examines how various feature types and feature selection strategies affect the functionality of the framework through thorough experiments. The experimental findings demonstrate that our suggested framework reaches the highest levels of accuracy and robustness. This suggests that it has a lot of potential to improve the accuracy and dependability of BIQA approaches. Additionally, its use is broadened to include image transmission, compression, and restoration. Overall, the results demonstrate our framework’s promise and ability to advance studies into image quality assessment.

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“…Versatile Video Coding (VVC) is the latest video coding standard, introduced in 2021 [2,3] which provided around 50% of compression improvement when compared to the prior High Efficiency Video Coding (HEVC) standard and its relevant 3D-HEVC extensions [4][5][6]. Therefore, VVC is a promising coding solution for compressing surveillance videos.…”

Section: Introductionmentioning

confidence: 99%

Learning adaptive motion search for fast versatile video coding in visual surveillance systems

Thanh,

Quang,

Huu

et al. 2023

IET Image Processing

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Visual surveillance systems have been playing an important role in monitoring and managing at public areas. However, the computational complexity of video compression in these applications is still a great challenge. To meet practical requirements, the authors propose in this paper a low‐complexity surveillance video coding solution in which the most recent Versatile Video Coding (VVC) standard is improved with a novel learning adaptive motion search algorithm. The proposed algorithm is designed based on the temporal motion and spatial texture characteristics of surveillance videos. First, the authors study and define a list of spatial and temporal features which indicates the motion and texture characteristics of surveillance video. These features are used together with a machine learning algorithm to appropriately assign a search range for the VVC motion search. Second, to reduce search points, the authors propose an adaptive Test Zone (TZ) search in which TZ steps are early terminated following the variation of spatial–temporal features. Performance evaluation conducted for a rich set of surveillance videos and relevant benchmarks have shown the superiority of the proposed method, notably with around 33% of encoding time saving when compared with the state‐of‐the art VVC solution and relevant benchmarks while asking for negligible compression loss.

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DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network

Cited by 41 publications

References 48 publications

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Adaptive Feature Fusion and Kernel-Based Regression Modeling to Improve Blind Image Quality Assessment

Learning adaptive motion search for fast versatile video coding in visual surveillance systems

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