Quality Assessment of UGC Videos Based on Decomposition and Recomposition

Liu, Yongxu; Wu, Jinjian; Li, Leida; Dong, Weisheng; Shi, Guang

doi:10.1109/tcsvt.2022.3209007

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…Therefore, a progressive aggregation (PA) module with residual structure is employed. The model can preserve the sensitive features of the scene and reduce the shallow feature dimension to achieve efficient aggregation [35].…”

Section: Progressive Aggregationmentioning

confidence: 99%

Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification

Zhao,

Liang,

Huang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Progressive Aggregationmentioning

confidence: 99%

Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification

Zhao,

Liang,

Huang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This method, also inspired by biology, is not yet widely used, due to the difficulty to train such models, but is gaining popularity for certain applications [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Traffic sign detection and recognition using event camera image reconstruction

Jeziorek¹,

Kryjak²

2022

Preprint

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<p>This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other on colour images. The best result was achieved for the model trained on the basis of greyscale images, achieving an efficiency of 87.03%.</p>

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“…and Jiang 2019), Patch-VQ(Ying et al 2021), BVQA-2022), DSD-PRO(Liu et al 2022a), FAST-VQA(Wu et al 2022), ZoomVQA(Zhao et al 2023b), and DOVER(Wu et al 2023e)). Following the setting in FAST-VQA, models are first trained on LSVQ training set, and the performance is validated on LSVQ test set, LSVQ 1080p set, KoNViD, and LIVE-VQC.…”

mentioning

confidence: 99%

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

Liu,

Quan,

Xiao

et al. 2024

AAAI

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Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current approaches have to adopt multi-branch models and take as input the multi-resolution data, which burdens the model complexity. In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size. The basic idea is to scale the data into a pyramid first, and reduce the pyramid into a regular data dimension with a masking strategy. Benefiting from the spatial and temporal redundancy in images and videos, the processed data maintains the multi-scale characteristics with a regular input size, thus can be processed by a single-branch model. We verify the sampling method in image and video quality assessment. Experiments show that our sampling method can improve the performance of current single-branch models significantly, and achieves competitive performance to the multi-branch models without extra model complexity. The source code will be available at https://github.com/Sissuire/SAMA.

show abstract

Quality Assessment of UGC Videos Based on Decomposition and Recomposition

Cited by 11 publications

References 48 publications

Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification

Hierarchical Deep Features Progressive Aggregation for Remote Sensing Images Scene Classification

Traffic sign detection and recognition using event camera image reconstruction

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

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