Construction of Video Quality Assessment Dataset for Deep-Sea Exploration

Fig. 2. Examples of five classes of contents about underwater videos. (a) Submarine rubbish; (b) submarine topography; (c) hydrothermal vents; (d) marine operation; (e) marine life

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Fig. 3. Original and Fusion enhanced underwater images. (a) Original image; (b) image enhanced with Fusion model

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Fig. 4. Original and Ucolor enhanced underwater images.(a) Original image; (b) image enhanced with Ucolor model

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Fig. 5. ROI area (inside the box) and non-ROI area (outside the box) of a single frame

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Fig. 6. Flow chart of subjective assessment of video quality

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Fig. 7. Subjective video quality assessment system. (a) Viewing interface; (b) assessment interface

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Fig. 8. Performance of underwater enhancement models in low light environment. (a) Original frame, MOS is 28.1; (b) enhanced by Fusion, MOS is 17.2; (c) enhanced by Ucolor, MOS is 20.9

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Fig. 9. Performance of underwater enhancement models in color cast environment. (a) Original frame, MOS is 43.6; (b) enhanced by Fusion, MOS is 58.4; (c) enhanced by Ucolor, MOS is 50.4

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Table 1. Details of video quality degradation parameters
View table
Table 1. Details of video quality degradation parameters
Type of degradation Parameter ROI Non-ROI
Gaussian blurring Kernel $5 \times 5$ $3 \times 3$
Sigma 3 1
Gaussian noise Mean 0 0
STD 11 8

Table 2. Subjective performance of video quality enhancement and degradation methods
View table
Table 2. Subjective performance of video quality enhancement and degradation methods
Method Enhancement model Degradation model
Ucolor Fusion ROI_GB ROI_GN
Percentage of quality scores higher or lower than the original video /% 35 33 -72 -67

Table 3. Performance comparison of different image/video quality assessment models on the dataset constructed in this paper

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Table 3. Performance comparison of different image/video quality assessment models on the dataset constructed in this paper

Classification of objective quality assessment model		Model	Correlation index
Classification of objective quality assessment model		Model	PLCC	SROCC
Underwater scene	Image	UCIQE^［22］	0.3259	0.2293
	Image	UIQM^［23］	0.3054	0.3272
	Image	Guo’s^［24］	0.4310	0.3219
	Video	Moreno-Roldán’s^［9］	0.3455	0.2590
	Video	Song’s^［6］	0.5103	0.4936
Terrestrial scene	Image	Brisque^［25］	0.3953	0.4239
	Video	VBliinds^［27］	0.6735	0.6336
	Video	VIIDEO^［26］	0.6423	0.6060

Table 4. Results of video quality assessment models on different underwater video datasets

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Table 4. Results of video quality assessment models on different underwater video datasets

Objective assessment model	PLCC/SROCC
Objective assessment model	Ours	Song’s dataset^［6］	Moreno-Roldán’s dataset^［8］
Song’s^［6］	0.51/0.49	0.84/0.83	—
Moreno-Roldán’s^［9］	0.35/0.26	—	0.80/0.76
VIIDEO^［26］	0.64/0.61	0.01/0.01	0.12/0.11

Table 5. Correlation analysis of video quality characteristics

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Table 5. Correlation analysis of video quality characteristics

Feature class	Feature name	Correlation
Spatial domain	Underwater image colorfulness（UIQM）	0.299**
Spatial domain	Underwater image contrast（UIQM）	0.284**
Natural image statistics	Brisque_1（Brisque）	-0.369**
	Brisque_2，3，4，6，8，10，12，14，16，18（Brisque）	-0.230~-0.312
	NIQE_4（VBliinds）	0.260**
	NIQE_5（VBliinds）	-0.216**
	NIQE_8（VBliinds）	0.247**
	NIQE_16（VBliinds）	0.292**
	NIQE_22（VBliinds）	0.216**
Frequency domain	DC_variation（VBliinds）	0.430**
Motion	Global motion（VBliinds）	-0.249**

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Wei Song, Xiaochen Liu, Dongmei Huang, Kelin Sun, Bing Zhang. Construction of Video Quality Assessment Dataset for Deep-Sea Exploration[J]. Laser & Optoelectronics Progress, 2022, 59(17): 1733001

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Paper Information

Category: Vision, Color, and Visual Optics

Received: Nov. 9, 2021

Accepted: Dec. 27, 2021

Published Online: Aug. 25, 2022

The Author Email: Xiaochen Liu (xiaochenliu96@163.com), Dongmei Huang (dmhuang@shou.edu.cn.com)

DOI:10.3788/LOP202259.1733001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Details of video quality degradation parameters

Table 1. Details of video quality degradation parameters

Table 2. Subjective performance of video quality enhancement and degradation methods

Table 2. Subjective performance of video quality enhancement and degradation methods

Table 3. Performance comparison of different image/video quality assessment models on the dataset constructed in this paper

Table 3. Performance comparison of different image/video quality assessment models on the dataset constructed in this paper

Table 4. Results of video quality assessment models on different underwater video datasets

Table 4. Results of video quality assessment models on different underwater video datasets

Table 5. Correlation analysis of video quality characteristics

Table 5. Correlation analysis of video quality characteristics