Objective assessment of image quality based on image content contrast perception

Jun-Cai Yao; Jing Shen

doi:10.7498/aps.69.20200335

Journals >Acta Physica Sinica >Volume 69 >Issue 14 >Page 148702-1 > Article

Acta Physica Sinica
Vol. 69, Issue 14, 148702-1 (2020)

Objective assessment of image quality based on image content contrast perception

Jun-Cai Yao^{1、2、3、*} and Jing Shen¹

Author Affiliations

¹School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China

²School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723000, China

³School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China

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DOI: 10.7498/aps.69.20200335 Cite this Article

Jun-Cai Yao, Jing Shen. Objective assessment of image quality based on image content contrast perception[J]. Acta Physica Sinica, 2020, 69(14): 148702-1 Copy Citation Text

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Fig. 1. The architecture of the proposed IQA method.

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Fig. 2. Scatter plots between the subjective and objective IQA results of images in four databases: (a) LIVE; (b) CSIQ; (c) TID2008; (d) TID2013

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Fig. 3. IQA results of the gray and monochrome images in IVC database by the proposed model.

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Fig. 4. Comparing the accuracy of the proposed model with those of the existing 7 models based on the IQA results in TID2008 database: (a) PSNR-TID2008; (b) VSNR-TID2008; (c) SSIM-TID2008; (d) FSIMc-TID2008; (e) VSI-TID2008; (f) GMSD-TID2008; (g) MAD-TID2008; (h) MPCC-TID2008.

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Fig. 5. Comparison of the complexity of 8 IQA models based on the IQA running time per 10 images.

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Fig. 6. Accuracy comparisons among 8 IQA metrics based on PLCC of IQA results from 28 types of distortion images in three databases: (a) CSIQ; (b) LIVE; (c) TID2008.

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Fig. 7. Scatter plots of the IQA results of 6 kinds of distorted images in CSIQ database evaluating by the proposed IQA model: (a) awgn; (b) jpeg; (c) jpeg2k; (d) fnoise; (e) blur; (f) contrast.

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Fig. 8. Scatter plots of the IQA results of 5 kinds of distorted images in LIVE database evaluating by the proposed IQA model: (a) jpeg2k; (b) jpeg; (c) WN; (d) gblur; (e) fastfading.

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Fig. 9. Scatter plots of the IQA results of 17 kinds of distorted images in TID2008 database evaluating by the proposed IQA model: (a) AGN; (b) ANCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC.

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Fig. 10. Scatter plots of the IQA results of 24 kinds of distorted images in TID2013 database evaluating by the proposed IQA model: (a) AGN; (b) NCC; (c) SCN; (d) MN; (e) HFN; (f) IN; (g) QN; (h) GB; (i) ID; (j) JPEG; (k) JPEG2k; (l) JPEGtrans; (m) JPEG2ktrans; (n) NEPN; (o) LBWD; (p) MS; (q) CC; (r) CCS; (s) MGN; (t) CN; (u) LCN; (v) CQWD; (w) CA; (x) SSR.

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数据库	LIVE(779)	CSIQ(866)	TID2008(1700)	TID2013(3000)	加权
PLCC	0.9622	0.9586	0.8778	0.8616	0.8915
SROCC	0.9660	0.9569	0.8831	0.8452	0.8854
RMSE	7.4397	0.0747	0.6427	0.6293	—
OR	0.1531	0.2690	0.1287	0.1198	—

Table 1. Calculated 4 correlation parameters between the subjective and objective IQA scores of images in 4 databases.

数据库	参数	PSNR	VSNR	SSIM	FSIMc	VSI	GMSD	MAD	MPCC
CSIQ	PLCC	0.8000	0.8002	0.8613	0.9192	0.9279	0.9541	0.9502	0.9587
	SROCC	0.8058	0.8106	0.8756	0.9310	0.9423	0.9570	0.9466	0.9569
	RMSE	0.1575	0.1575	0.1334	0.1034	0.0979	0.0786	0.0818	0.0748
	OR	0.4220	0.3832	0.3535	0.3041	0.2873	0.2742	0.2829	0.2738
LIVE	PLCC	0.8723	0.9231	0.9449	0.9613	0.9482	0.9603	0.9675	0.9620
	SROCC	0.8756	0.9274	0.9479	0.9645	0.9524	0.9603	0.9669	0.9660
	RMSE	13.3597	10.5059	8.9455	7.5296	8.6816	7.6214	6.9073	7.4598
	OR	0.2179	0.2151	0.1865	0.1627	0.1853	0.1643	0.1529	0.1606
TID2013	PLCC	0.7062	0.7402	0.7895	0.8769	0.9000	0.8553	0.8267	0.8648
	SROCC	0.6917	0.7316	0.7417	0.8510	0.8965	0.8044	0.7807	0.8452
	RMSE	0.8887	0.8392	0.7608	0.5959	0.5404	0.6423	0.6975	0.6224
	OR	0.1636	0.1552	0.1427	0.1132	0.1045	0.1242	0.1323	0.1179

Table 2.

Comparing the accuracy of the proposed model with those of the existing 7 models based on the IQA results in CSIQ, LIVE, and TID2013 databases.

基于CSIQ, LIVE和TID2013数据库中的图像IQA结果比较所提模型与现有7个模型的精度

失真类别	PSNR	VSNR	SSIM	FSIMc	VSI	GMSD	MAD	MPCC
1 Additive Gaussian noise(AGN)	0.9552	0.8319	0.8685	0.9152	0.9527	0.9503	0.8897	0.8706
2 Noise in color comp. (NCC)	0.9256	0.7814	0.8050	0.8873	0.9172	0.9118	0.8438	0.8324
3 Spatially correl. noise (SCN)	0.9525	0.8105	0.8621	0.8989	0.9472	0.9391	0.9008	0.7457
4 Masked noise (MN)	0.8707	0.7715	0.8219	0.8492	0.8203	0.7547	0.8009	0.6943
5 High frequency noise (HFN)	0.9731	0.9061	0.9081	0.9475	0.9655	0.9567	0.9233	0.9090
6 Impulse noise (IN)	0.8887	0.7442	0.7415	0.8171	0.8635	0.7572	0.3206	0.7408
7 Quantization noise (QN)	0.8880	0.8384	0.8702	0.8794	0.8747	0.9110	0.8571	0.8122
8 Gaussian blur (GB)	0.9169	0.9437	0.9634	0.9544	0.9551	0.9099	0.9357	0.9252
9 Image denoising (ID)	0.9640	0.9463	0.9589	0.9652	0.9707	0.9759	0.9645	0.9594
10 JPEG compression (JPEG)	0.9167	0.9386	0.9551	0.9754	0.9858	0.9843	0.9638	0.9509
11 JPEG2000 compression (JPEG2 K)	0.9170	0.9513	0.9658	0.9754	0.9845	0.9812	0.9740	0.9452
12 JPEG transm. errors (JPEG trans.)	0.8104	0.8597	0.9181	0.9176	0.9457	0.9079	0.9001	0.8805
13 JPEG2000 transm. errors (JPEG2K trans)	0.9002	0.8435	0.8801	0.8929	0.9192	0.9085	0.8838	0.8699
14 Non ecc. patt. noise (NEPN)	0.6746	0.6774	0.7773	0.8068	0.8162	0.8133	0.8608	0.8132
15 Local block-wise dist. (LBWD)	0.2410	0.3632	0.6022	0.5542	0.4984	0.6520	0.4187	0.6845
16 Mean shift (MS)	0.8056	0.5160	0.8019	0.7869	0.8021	0.7707	0.6934	0.7720
17 Contrast change (CC)	0.5811	0.4251	0.6026	0.7266	0.6974	0.7111	0.3199	0.8108
18 Change of color saturation (CSS)	0.3294	0.4184	0.4590	0.8228	0.8052	0.4234	0.2846	0.7583
19 Multipl. Gauss. noise (MGN)	0.9204	0.7730	0.7896	0.8660	0.9136	0.8911	0.8529	0.8759
20 Comfort noise (CN)	0.8702	0.9016	0.9022	0.9463	0.9546	0.9562	0.9444	0.8476
21 Lossy compr. of noisy (LCN)	0.9429	0.8960	0.9174	0.9564	0.9636	0.9703	0.9562	0.7889
22 Image color quant. w. dither (CQWD)	0.9308	0.8773	0.8619	0.8911	0.8963	0.9192	0.8779	0.8721
23 Chromatic aberrations (CA)	0.9556	0.9592	0.9770	0.9794	0.9748	0.9737	0.9696	0.9473
24 Sparse sampl. and reconstr. (SSR)	0.9296	0.9477	0.9667	0.9776	0.9808	0.9849	0.9766	0.9349
Max	0.9731	0.9592	0.9770	0.9794	0.9858	0.9849	0.9766	0.9594
Min	0.2410	0.3632	0.4590	0.5542	0.4984	0.4234	0.2846	0.6845
波动范围宽度	0.7321	0.5959	0.5181	0.4252	0.4873	0.5614	0.6920	0.2750
所有整体精度	0.7062	0.7402	0.7895	0.8769	0.9000	0.8553	0.8267	0.8648

Table 3. [in Chinese]

Jun-Cai Yao, Jing Shen. Objective assessment of image quality based on image content contrast perception[J]. Acta Physica Sinica, 2020, 69(14): 148702-1

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