No-reference light field image quality assessment based on joint spatial-angular information

Bin Wang; Yongqiang Bai; Zhongjie Zhu; Mei Yu; Gangyi Jiang

doi:10.12086/oee.2024.240139

Opto-Electronic Engineering, Volume. 51, Issue 9, 240139-1(2024)

No-reference light field image quality assessment based on joint spatial-angular information

Bin Wang1... Yongqiang Bai2, Zhongjie Zhu2, Mei Yu1,* and Gangyi Jiang1 |Show fewer author(s)

Author Affiliations

¹Faculty of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China

²College of Information and Intelligent Engineering, Zhejiang Wanli University, Ningbo, Zhejiang 315100, China

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Figures & Tables(11)

Fig. 1. Different representations of light field image. (a) MLI; (b) SAIs

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Fig. 2. Overall framework of SAE-BLFI

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Fig. 3. Schematic diagram of spatial-angular separation

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Fig. 4. EPI under different distortion conditions for two scenarios

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Fig. 5. Boxplot of SROCC distribution in K-fold cross-validation on Win5-LID and NBU-LF1.0 datasets. (a) Win5-LID; (b) NBU-LF1.0

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Fig. 6. F-test statistical significance analysis on Win5-LID and NBU-LF1.0 datasets. (a) Win5-LID; (b) NBU-LF1.0

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Table 1. Overall performance comparison of different methods on different LFI datasets

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Table 1. Overall performance comparison of different methods on different LFI datasets

Types	Methods	Win5-LID			NBU-LF1.0			SHU
Types	Methods	PLCC↑	SROCC↑	RMSE↓	PLCC↑	SROCC↑	RMSE↓	PLCC↑	SROCC↑	RMSE↓
NR 2D-IQA	BRISQUE^[24]	0.6217	0.4537	0.7604	0.4989	0.3871	0.7879	0.9011	0.8883	0.4591
NR 2D-IQA	GLBP^[25]	0.5357	0.4150	0.8130	0.5056	0.3490	0.7647	0.7168	0.6565	0.7504
FR LF-IQA	MDFM^[5]	0.7763	0.7471	0.6249	0.7888	0.7559	0.5649	0.8947	0.8908	0.4863
	Min's^[7]	0.7281	0.6645	0.6874	0.7104	0.6579	0.6439	0.8497	0.8470	0.5757
	Meng's^[26]	0.6983	0.6347	0.7203	0.8404	0.7825	0.4889	0.9279	0.9203	0.4039
NR LF-IQA	BELIF^[27]	0.5751	0.5059	0.7865	0.7014	0.6389	0.6276	0.8967	0.8656	0.4803
	NR-LFQA^[8]	0.7298	0.6979	0.6271	0.8528	0.8113	0.4658	0.9224	0.9229	0.4132
	Tensor-NLFQ^[12]	0.5813	0.4885	0.7706	0.6884	0.6246	0.6305	0.9307	0.9061	0.3857
	VBLFI^[10]	0.7213	0.6704	0.6843	0.8027	0.7539	0.5218	0.9235	0.8996	0.4064
	4D-DCT-LFIQA^[2]	0.8234	0.8074	0.5446	0.8395	0.8217	0.4871	0.9400	0.9320	0.3691
	DeeBLiF^[18]	0.8427	0.8186	0.5160	0.8583	0.8229	0.4588	0.9548	0.9419	0.3185
	SATV-BLiF^[28]	0.7933	0.7704	0.5842	0.8515	0.8237	0.4686	0.9332	0.9284	0.3897
	Proposed	0.8653	0.8451	0.4863	0.9108	0.8937	0.3658	0.9649	0.9547	0.2808

Table 2. SROCC values for different distortion types across various methods on Win5-LID and NBU-LF1.0 datasets

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Table 2. SROCC values for different distortion types across various methods on Win5-LID and NBU-LF1.0 datasets

Types	Methods	Win5-LID				NBU-LF1.0					Hit count
Types	Methods	HEVC	JEPG2K	LN	NN	NN	BI	EPICNN	MDR	VDSR	Hit count
NR 2DIQA	BRISQUE^[24]	0.5641	0.7801	0.5222	0.2462	0.3435	0.4145	0.5795	0.4331	0.7937	0
NR 2DIQA	GLBP^[25]	0.7165	0.4853	0.4678	0.3011	0.3229	0.3995	0.4344	0.4478	0.7381	0
FR LF-IQA	MDFM^[5]	0.7922	0.7669	0.6437	0.6692	0.8025	0.9089	0.7899	0.7386	0.8709	1
	Min's^[7]	0.6997	0.6507	0.6159	0.6288	0.8156	0.8667	0.7361	0.7963	0.9376	1
	Meng's^[26]	0.8886	0.6939	0.8459	0.8001	0.7429	0.9018	0.7997	0.5783	0.9225	2
NR LF-IQA	BELIF^[27]	0.7666	0.6379	0.6097	0.5452	0.7680	0.7122	0.6874	0.6128	0.7989	0
	NR-LFQA^[8]	0.7571	0.7338	0.6362	0.7026	0.8930	0.8807	0.7653	0.6111	0.8164	0
	Tensor-NLFQ^[12]	0.6853	0.5799	0.5663	0.5897	0.6946	0.7203	0.5245	0.5417	0.8018	0
	VBLFI^[10]	0.7141	0.7449	0.6908	0.7197	0.8316	0.8372	0.7195	0.4613	0.9134	0
	4D-DCT-LFIQA^[2]	0.8698	0.8946	0.8127	0.8235	0.9040	0.8719	0.7100	0.8095	0.8882	2
	DeeBLiF^[18]	0.9648	0.8195	0.7928	0.8306	0.9184	0.8876	0.7248	0.6961	0.8857	3
	SATV-BLiF^[28]	0.7918	0.8685	0.7566	0.8525	0.9282	0.9190	0.7722	0.6498	0.8617	2
	Proposed	0.9417	0.8955	0.8472	0.8742	0.9165	0.9153	0.7749	0.8443	0.9294	7

Table 3. Ablation experiments of different functional modules on Win5-LID and NBU-LF1.0 datasets

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Table 3. Ablation experiments of different functional modules on Win5-LID and NBU-LF1.0 datasets

	Win5-LID			NBU-LF1.0
	PLCC	SROCC	RMSE	PLCC	SROCC	RMSE
SF	0.8338	0.8179	0.5217	0.8853	0.8654	0.4104
AF	0.8195	0.7992	0.5322	0.8709	0.8566	0.4208
EF	0.7950	0.7992	0.5652	0.8637	0.8478	0.4176
SF+AF	0.8513	0.8285	0.5057	0.9057	0.8857	0.3845
SF+AF+EF	0.8653	0.8451	0.4863	0.9108	0.8937	0.3658

Table 4. Comparison of running time for different NR LF-IQA methods

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Table 4. Comparison of running time for different NR LF-IQA methods

Methods	Platform	Device	Time/s
BELIF^[27]	Matlab	CPU	167.60
NR-LFQA^[8]	Matlab	CPU	220.92
Tensor-NLFQ^[12]	Matlab	CPU	630.47
VBLFI^[10]	Matlab	CPU	68.48
4D-DCT-LFIQA^[2]	Matlab	CPU	148.29
DeeBLiF^[18]	Pytorch	GPU	2.77
SATV-BLiF^[28]	Matlab	CPU	4.38
Proposed	Pytorch	GPU	3.77

Table 5. The results of training the model on the Win5-LID dataset and testing it on the NBU-LF1.0 and SHU datasets
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Table 5. The results of training the model on the Win5-LID dataset and testing it on the NBU-LF1.0 and SHU datasets
Methods NBU-LF1.0 (NN) SHU (JPEG2000)
PLCC SROCC PLCC SROCC
4D-DCT-LFIQA 0.7753 0.7040 0.7824 0.7967
DeeBLiF 0.8253 0.7265 0.7609 0.7252
Proposed 0.9082 0.8610 0.8821 0.8717

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Bin Wang, Yongqiang Bai, Zhongjie Zhu, Mei Yu, Gangyi Jiang. No-reference light field image quality assessment based on joint spatial-angular information[J]. Opto-Electronic Engineering, 2024, 51(9): 240139-1

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Paper Information

Category: Article

Received: Jun. 14, 2024

Accepted: Aug. 18, 2024

Published Online: Dec. 12, 2024

The Author Email:

DOI:10.12086/oee.2024.240139

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Overall performance comparison of different methods on different LFI datasets

Table 1. Overall performance comparison of different methods on different LFI datasets

Table 2. SROCC values for different distortion types across various methods on Win5-LID and NBU-LF1.0 datasets

Table 2. SROCC values for different distortion types across various methods on Win5-LID and NBU-LF1.0 datasets

Table 3. Ablation experiments of different functional modules on Win5-LID and NBU-LF1.0 datasets

Table 3. Ablation experiments of different functional modules on Win5-LID and NBU-LF1.0 datasets

Table 4. Comparison of running time for different NR LF-IQA methods

Table 4. Comparison of running time for different NR LF-IQA methods

Table 5. The results of training the model on the Win5-LID dataset and testing it on the NBU-LF1.0 and SHU datasets

Table 5. The results of training the model on the Win5-LID dataset and testing it on the NBU-LF1.0 and SHU datasets

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