YANG Kaifang, CHAO Xuemin, MENG Qinqin, et al. Research on the Perceptual Distortion of Chinese Text Screen Content Image Using Versatile Video Coding[J]. 2024, 58(4): 18-31.
DOI:
YANG Kaifang, CHAO Xuemin, MENG Qinqin, et al. Research on the Perceptual Distortion of Chinese Text Screen Content Image Using Versatile Video Coding[J]. 2024, 58(4): 18-31.DOI: 10.7652/xjtuxb202404002.
Research on the Perceptual Distortion of Chinese Text Screen Content Image Using Versatile Video Coding
To explore the influence of the state-of-the-art versatile video coding(VVC)on the perceptual quality of the Chinese text screen content image(TSCI)
image subjective observation experiments were designed to study the perceptual distortion of Chinese TSCI using VVC based on the hybrid coding framework principles of VVC. A Chinese text screen content image dataset(CT-SCID)was constructed and image subjective observation experiments were designed to analyze the types and development paths of Chinese TSCI perceptual distortion caused by VVC. By combining the hybrid coding framework principles of VVC
factors that affect the degree of perceptual distortion of Chinese TSCI using VVC were theoretically analyzed and experimentally verified. The performance of representative screen content image quality evaluation methods for evaluating the perceptual distortion of Chinese TSCI using VVC was summarized. Experimental results show that font size and contrast are crucial factors affecting the perceptual quality of Chinese TSCI using VVC. A smaller font size and lower contrast of Chinese TSCI shall lead to a lower perceptual quality of the image. The existing representative screen content image quality evaluation methods are unable to provide quality evaluation results that fully conform to human visual perception characteristics. The research has some guiding significance for the subsequent development of perceptual quality evaluation methods and efficient coding methods applicable to Chinese TSCI.
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TANG Tong, LI Ling, WU Xiaoyu, et al. TSA-SCC: text semantic-aware screen content coding with ultra low bitrate [J]. IEEE Transactions on Image Processing, 2022, 31: 2463-2477.
NI Zhangkai, MA Lin, ZENG Huanqiang, et al. ESIM: edge similarity for screen content image quality assessment [J]. IEEE Transactions on Image Processing, 2017, 26(10): 4818-4831.
YANG Huan, FANG Yuming, LIN Weisi. Perceptual quality assessment of screen content images [J]. IEEE Transactions on Image Processing, 2015, 24(11): 4408-4421.
GONG Yanchao, YANG Kaifang, LIU Ying, et al. Quantization parameter cascading for surveillance video coding considering all inter reference frames [J]. IEEE Transactions on Image Processing, 2021, 30: 5692-5707.
WANG Shiqi, GU Ke, ZHANG Xiang, et al. Subjective and objective quality assessment of compressed screen content images [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2016, 6(4): 532-543.
YANG Jiachen, BIAN Zilin, ZHAO Yang, et al. Full-reference quality assessment for screen content images based on the concept of global-guidance and local-adjustment [J]. IEEE Transactions on Broadcasting, 2021, 67(3): 696-709.
CHENG Shan, ZENG Huanqiang, CHEN Jing, et al. Screen content video quality assessment: subjective and objective study [J]. IEEE Transactions on Image Processing, 2020, 29: 8636-8651.
LI Teng, MIN Xiongkuo, ZHAO Heng, et al. Subjective and objective quality assessment of compressed screen content videos [J]. IEEE Transactions on Broadcasting, 2021, 67(2): 438-449.
FANG Yuming, YAN Jiebin, LIU Jiaying, et al. Objective quality assessment of screen content images by uncertainty weighting [J]. IEEE Transactions on Image Processing, 2017, 26(4): 2016-2027.
JIANG Xuhao, SHEN Liquan, FENG Guorui, et al. An optimized CNN-based quality assessment model for screen content image [J]. Signal Processing: Image Communication, 2021, 94: 116181.
JAKHETIYA V, GU Ke, LIN Weisi, et al. A prediction backed model for quality assessment of screen content and 3-D synthesized images [J]. IEEE Transactions on Industrial Informatics, 2018, 14(2): 652-660.
WANG Cunrui, DING Yang, LIU Yu, et al. Chinese font generation from stroke semantic and attention mechanism [J]. Journal of Computer-Aided Design Computer Graphics, 2022, 34(8): 1229-1237.
ZHANG Jijia.On the relation of entirety and components in identifying process of Chinese characters and words [J]. Journal of Dialectics of Nature, 2002, 24(3): 91-94.
苏培成. 现代汉字学的学科建设 [J]. 语言文字应用, 2007(2): 2-11.
SU Peicheng.The discipline building of modern Chinese character study [J]. Applied Linguistics, 2007(2): 2-11.
LI Wei, REN Peng, ZHAO Fan, et al. A modified method for intra-coding rate control in high efficiency video coding [J]. Journal of Xi'an Jiaotong University, 2019, 53(4): 79-84.
SONG Beibei, HE Fan, MA Suina, et al. An image compression algorithm with high-precision adaptive rate control [J]. Journal of Xi'an Jiaotong University, 2022, 56(2): 198-206.
BROSS B, WANG Yekui, YE Yan, et al. Overview of the versatile video coding(VVC)standard and its applications [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(10): 3736-3764.
SHI Sheng, ZHANG Xiang, WANG Shiqi, et al. Study on subjective quality assessment of Screen Content Images [C]//2015 Picture Coding Symposium(PCS).Piscataway, NJ, USA: IEEE, 2015: 75-79.
ITU. Methodology for the subjective assessment of video quality in multimedia applications: BT.1788 [S]. Geneva: ITU, 2007: 1-13.
ITU ISO/IEC.VTM reference software for VVC [EB/OL]. [2022-05-03]. https: //vcgit.hhi.fraunhofer.de/jvet/VVCSoftw are_VTM/-/tree/VTM-16.2.
BROSS B, CHEN Jianle, OHM J R, et al. Developments in international video coding standardization after AVC, with an overview of versatile video coding(VVC)[J]. Proceedings of the IEEE, 2021, 109(9): 1463-1493.
WEI Chuanyi, CHEN Qin, ZHANG Min. Research on document image layout segmentation algorithm based on projection [J]. Modern Computer, 2016(10): 33-38.
NI Zhangkai, ZENG Huanqiang, MA Lin, et al. A Gabor feature-based quality assessment model for the screen content images [J]. IEEE Transactions on Image Processing, 2018, 27(9): 4516-4528.
LIU Anmin, LIN Weisi, NARWARIA M. Image quality assessment based on gradient similarity [J]. IEEE Transactions on Image Processing, 2012, 21(4): 1500-1512.
NI Zhangkai, MA Lin, ZENG Huanqiang, et al. Gradient direction for screen content image quality assessment [J]. IEEE Signal Processing Letters, 2016, 23(10): 1394-1398.
FU Ying, ZENG Huanqiang, MA Lin, et al. Screen content image quality assessment using multi-scale difference of Gaussian [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(9): 2428-2432.
YANG Qi, MA Zhan, XU Yiling, et al. Modeling the screen content image quality via multiscale edge attention similarity [J]. IEEE Transactions on Broadcasting, 2020, 66(2): 310-321.
WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.