TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate
Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information avera...
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| Published in | IEEE transactions on image processing Vol. 31; pp. 2463 - 2477 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to <inline-formula> <tex-math notation="LaTeX">3\times </tex-math></inline-formula> at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates. |
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| AbstractList | Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates. Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to <inline-formula> <tex-math notation="LaTeX">3\times </tex-math></inline-formula> at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates. Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to [Formula Omitted] at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates. Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates. |
| Author | Tang, Tong Li, Ling Chen, Ruizhi Cheng, Limin Lu, Guo Wu, Xiaoyu Li, Haochen |
| Author_xml | – sequence: 1 givenname: Tong orcidid: 0000-0003-0616-2003 surname: Tang fullname: Tang, Tong email: tangtong@cqupt.edu.cn organization: School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, the Advanced Network and Intelligent Interconnection Technology Key Laboratory of Chongqing Education Commission of China, and the Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, China – sequence: 2 givenname: Ling orcidid: 0000-0001-8877-9052 surname: Li fullname: Li, Ling email: liling@iscas.ac.cn organization: Chinese Academy of Sciences, Institute of Software, Beijing, China – sequence: 3 givenname: Xiaoyu surname: Wu fullname: Wu, Xiaoyu email: xiaoyu2019@iscas.ac.cn organization: Chinese Academy of Sciences, Institute of Software, Beijing, China – sequence: 4 givenname: Ruizhi orcidid: 0000-0001-7219-4658 surname: Chen fullname: Chen, Ruizhi email: ruizhi@iscas.ac.cn organization: Chinese Academy of Sciences, Institute of Software, Beijing, China – sequence: 5 givenname: Haochen orcidid: 0000-0003-0813-6351 surname: Li fullname: Li, Haochen email: haochen2021@iscas.ac.cn organization: Chinese Academy of Sciences, Institute of Software, Beijing, China – sequence: 6 givenname: Guo surname: Lu fullname: Lu, Guo email: sdluguo@gmail.com organization: School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China – sequence: 7 givenname: Limin surname: Cheng fullname: Cheng, Limin email: chenglimin@iscas.ac.cn organization: Chinese Academy of Sciences, Institute of Software, Beijing, China |
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| SubjectTerms | Bit rate Blurring Coding Computer & video games Encoding high efficiency video coding (HEVC) Image coding Image color analysis low bitrate Neural networks Object recognition Screen content coding (SCC) Semantics Text recognition text semantics Texts Video compression |
| Title | TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate |
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