No-reference quality metric for high dynamic range imaging system based on curvature analysis in tensor domain

• Autorzy: Jiang Gangyi , Yu Jiaowen , Yu Mei , Song Yang , Peng Zongju , Chen Fen

Identyfikatory

DOI

10.37190/oa190401

• Języki publikacji: EN

Abstrakty

EN

High dynamic range imaging systems can offer a more complete representation of scene, aiming to capture all brightness information of a visible range of scene, even in extreme lighting conditions.This paper proposes a no-reference quality metric for high dynamic range image (HDRI), in which a combination of tensor decomposition and curvature analysis is used to construct an efficient featureset that is sensitive to degradation levels of patches in HDRIs. Tensor decomposition maintains the majority of color information of an HDRI, and the geometric structure information of the HDRI is then extracted by a curvature analysis. A quality-related label feature matrix is subsequently defined and obtained by using a feature set and a sparse dictionary with quality-related labels. Then, the multi-dimensional local feature set of the HDRI is determined from the quality-related label feature matrix. Finally, the local feature set and other global feature set are pooled to predict the quality of the HDRI. The prediction performance of the proposed metric is verified by three public test databases, and the experimental results indicate that both its Pearson linear correlation coefficientand Spearman rank-order correlation coefficient are better than those of other no-reference metrics.The proposed metric produces statistically better assessment results, implying a higher consistency with human visual perception.

Słowa kluczowe

EN

high dynamic range image; no-reference quality metric; tensor domain; curvature analysis; quality-related label feature matrix

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2019
• Tom: Vol. 49, nr 4
• Strony: 527--543
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Jiang Gangyi

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China
• National Key Lab of Software New Technology, Nanjing University, Nanjing, China

autor

Yu Jiaowen

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China

autor

Yu Mei

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China
• National Key Lab of Software New Technology, Nanjing University, Nanjing, China

autor

Song Yang

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China

autor

Peng Zongju

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China

autor

Chen Fen

• Faculty of Information Science and Engineering, Ningbo University, Ningbo, China

Bibliografia

• [1] CHALMERS A., DEBATTISTA K., HDR video past, present and future: a perspective, Signal Processing: Image Communication 54, 2017, pp. 49–55, DOI:10.1016/j.image.2017.02.003.
• [2] MANTIUK R.K., Practicalities of predicting quality of high dynamic range images and video, [In] 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 904–908, DOI:10.1109/ICIP.2016.7532488.
• [3] HANHART P., BERNARDO M.V, PEREIRA M., PINHEIRO A.M.G., EBRAHIMI T., Benchmarking of objective quality metrics for HDR image quality assessment, EURASIP Journal on Image and Video Processing2015(1), 2015, article ID 39, DOI:10.1186/s13640-015-0091-4.
• [4] ZERMAN E., VALENZISE G., DUFAUX F., An extensive performance evaluation of full-reference HDR image quality metrics, Quality and User Experience 2(1), 2017, article ID 5, DOI:10.1007/s41233-017-0007-4.
• [5] AYDIN T.O., MANTIUK R., SEIDEL H.-P., Extending quality metrics to full luminance range images, Proceedings of SPIE 6806, 2008, article ID 68060B, DOI:10.1117/12.765095.
• [6] MANTIUK R., DALY S.J., MYSZKOWSKI K., SEIDEL H.-P., Predicting visible differences in high dynamicrange images: model and its calibration, Proceedings of SPIE 5666, 2005, pp. 204–214, DOI:10.1117/12.586757.
• [7] MANTIUK R., KIL JOONG KIM, REMPEL A.G., HEIDRICH W., HDR-VDP-2: a calibrated visual metricfor visibility and quality predictions in all luminance conditions, ACM Transactions on Graphics 30(4), 2011, article ID 40, DOI:10.1145/2010324.1964935.
• [8] NARWARIA M., MANTIUK R., DA SILVA M., LE CALLET P., HDR-VDP-2.2: a calibrated method forobjective quality prediction of high-dynamic range and standard images, Journal of ElectronicImaging 24(1), 2015, article ID 010501, DOI:10.1117/1.JEI.24.1.010501.
• [9] AYDIN T.O., MANTIUK R., MYSZKOWSKI K., SEIDEL H.-P., Dynamic range independent image qualityassessment, ACM Transactions on Graphics 27(3), 2008, article ID 69, DOI:10.1145/1360612.1360668.
• [10] NARWARIA M., DA SILVA M.P., LE CALLET P., HDR-VQM: An objective quality measure for high dynamic range video, Signal Processing: Image Communication 35, 2015, pp. 46–60, DOI:10.1016/j.jvcir.2018.10.016.
• [11] JINGTAO XU, PENG YE, QIAOHONG LI, HAIQING DU, YONG LIU, DOERMANN D., Blind image quality assessment based on high order statistics aggregation, IEEE Transactions on Image Processing 25(9),2016, pp. 4444–4457, DOI:10.1109/TIP.2016.2585880.
• [12] WUFENG XUE, XUANQIN MOU, LEI ZHANG, BOVIK A.C., XIANGCHU FENG, Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features, IEEE Transactions onImage Processing 23(11), 2014, pp. 4850–4862, DOI:10.1109/TIP.2014.2355716.
• [13] QINGBO WU, HONGLIANG LI, FANMAN MENG, NGAN K.N., Q-DNN: a quality-aware deep neural network for blind assessment of enhanced images, [In] 2016 Visual Communications and Image Processing (VCIP), 2017, pp. 1–4, DOI:10.1109/VCIP.2016.7805579.
• [14] NARWARIA M., DA SILVA M.P., LE CALLET P., PEPION R., Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality, Optical Engineering 52(10), 2013, article ID 102008, DOI:10.1117/1.OE.52.10.102008.
• [15] KORSHUNOV P., HANHART P., RICHTER T., ARTUSI A., MANTIUK R., EBRAHIMI T., Subjective quality assessment database of HDR images compressed with JPEG XT, [In] 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), 2015, pp. 1–6, DOI:10.1109/QoMEX.2015.7148119.
• [16] YI PENG, DEYU MENG, ZONGBEN XU, CHENQIANG GAO, YI YANG, BIAO ZHANG, Decomposable non-local tensor dictionary learning for multispectral image denoising, [In] 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2949–2956, DOI:10.1109/CVPR.2014.377.
• [17] LIN ZHANG, YING SHEN, HONGYU LI, JIANWEI LU, 3D palmprint identification using block-wise features and collaborative representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(8), 2015, pp. 1730–1736, DOI:10.1109/TPAMI.2014.2372764.
• [18] ZHOU WANG, BOVIK A.C., SHEIKH H.R., SIMONCELLI E.P., Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing 13(4), 2004, pp. 600–612,DOI:10.1109/TIP.2003.819861.
• [19] LIN ZHANG, LEI ZHANG, XUANQIN MOU, RFSIM: a feature based image quality assessment metric using Riesz transforms, [In] 2010 IEEE International Conference on Image Processing, 2010, pp. 321–324, DOI:10.1109/ICIP.2010.5649275.
• [20] LIN ZHANG, LEI ZHANG, XUANQIN MOU, ZHANG D., FSIM: a feature similarity index for image quality assessment, IEEE Transactions on Image Processing 20(8), 2011, pp. 2378–2386, DOI:10.1109/TIP.2011.2109730.
• [21] RUBINSTEIN R., PELEG T., ELAD M., Analysis K-SVD: a dictionary-learning algorithm for the analysis sparse model, IEEE Transactions on Signal Processing 61(3), 2013, pp. 661–677, DOI:10.1109/TSP.2012.2226445.
• [22] MITTAL A., MOORTHY A.K., BOVIK A.C., No-reference image quality assessment in the spatial domain, IEEE Transactions on Image Processing 21(12), 2012, pp. 4695–4708, DOI:10.1109/TIP.2012.2214050.
• [23] SOO-CHANG PEI, LI-HENG CHEN, Image quality assessment using human visual DOG model fused with random forest, IEEE Transactions on Image Processing 24(11), 2015, pp. 3282–3292, DOI:10.1109/TIP.2015.2440172.
• [24] ITU-T P.1401, Methods, metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models, International Telecommunication Union, 2012.
• [25] MOORTHY A.K., BOVIK A.C., Blind image quality assessment: from natural scene statistics to perceptual quality, IEEE Transactions on Image Processing 20(12), 2011, pp. 3350–3364, DOI:10.1109/TIP.2011.2147325.
• [26] SAAD M.A., BOVIK A.C., CHARRIER C., Blind image quality assessment: a natural scene statistics approach in the DCT domain, IEEE Transactions on Image Processing 21(8), 2012, pp. 3339–3352,DOI:10.1109/TIP.2012.2191563.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).