Screen Content Coding

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February 14, 2018, at 03:10 PM EST by 172.16.33.241 -
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[C4] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

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[C4] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

Last Update by Fanyi Duanmu, 02/14/2018

February 14, 2018, at 03:09 PM EST by 172.16.33.241 -
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In this project, we are collaborating with Huawei researchers and exploring fast screen content encoding and transcoding solutions to accelerate screen content compression, while simultaneously preserving the rate-distortion performance, including:

(1) Fast Screen Content Encoding System Design using Machine Learning Techniques.

(2) Fast HEVC-SCC Transcoding System Design using Machine Learning Techniques.

(3) Fast SCC-HEVC Transcoding System Design using Statistical Mode Mapping Techniques.

to:

In this project, we are collaborating with Huawei researchers and exploring fast screen content encoding and transcoding solutions to accelerate screen content compression, while simultaneously preserving the rate-distortion performance, including: (1) Fast Screen Content Encoding System Design using Machine Learning Techniques; (2) Fast HEVC-SCC Transcoding System Design using Machine Learning Techniques; and (3) Fast SCC-HEVC Transcoding System Design using Statistical Mode Mapping Techniques.

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In this project, we are exploring fast screen content encoding and transcoding solutions to accelerate screen content compression, while simultaneously preserving the rate-distortion performance, including:

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In this project, we are collaborating with Huawei researchers and exploring fast screen content encoding and transcoding solutions to accelerate screen content compression, while simultaneously preserving the rate-distortion performance, including:

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[P1] H. Yu, M. Xu, W. Wang, F. Duanmu, and S. Minaee, “Advanced Coding Techniques For High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) Extensions”, 2016.

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[J2] F. Duanmu, Z. Ma, and Y. Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2016 Aug; Vol: PP, Issue 99, Page:1-15. doi: 10.1109/JETCAS.2016.2597698

[C1] F. Duanmu, Z. Ma, and Y. Wang “A Novel Screen Content Fast Transcoding Framework Based on Statistical Study and Machine Learning”, in Proc. International Conference of Image Processing (ICIP), pp. 4205-4209, Phoenix, Arizona, USA, 2016.

[C2] F. Duanmu, Z. Ma, and Y. Wang, “Fast CU partition decision using machine learning for screen content compression”, Proc. IEEE International Conference on Image Processing (ICIP), pp. 4972 - 4976, Quebec City, Canada, 2015.

[C3] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

to:

[J2] F. Duanmu, Z. Ma, and Y. Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2016 Aug; Vol: PP, Issue 99, Page:1-15. doi: 10.1109/JETCAS.2016.2597698.

[C1] F. Duanmu, M. Xu, Y. Wang, and Z. Ma, “HEVC-Compliant Screen Content Transcoding Based on Mode Mapping and Fast Termination,” in Proc. of IEEE Visual Communications and Image Processing (VCIP), Petersburg, Florida, USA, 2017.

[C2] F. Duanmu, Z. Ma, and Y. Wang “A Novel Screen Content Fast Transcoding Framework Based on Statistical Study and Machine Learning”, in Proc. International Conference of Image Processing (ICIP), pp. 4205-4209, Phoenix, Arizona, USA, 2016.

[C3] F. Duanmu, Z. Ma, and Y. Wang, “Fast CU partition decision using machine learning for screen content compression”, Proc. IEEE International Conference on Image Processing (ICIP), pp. 4972 - 4976, Quebec City, Canada, 2015. [C4] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

February 14, 2018, at 03:03 PM EST by 172.16.33.241 -
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To exploit the unique signal characteristics of screen content and develop efficient SC compression solutions, the ISO/IEC Moving Picture Expert Group and the ITU-T Video Coding Experts Group, also referred as “Joint Collaborative Team on Video Coding” (JCTVC), has launched the standardization of SCC extension on top of the latest video standard - High Efficiency Video Coding (HEVC) since January 2014 and this extension is concluded in 2016 with significant research efforts involved from both academia and industry.

To better understand the natural statistics of screen contents and develop potential compression technologies, JCTVC launched the development of screen content extension on top of HEVC in early 2014. Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model software (i.e., SCM) already outperforms conventional HEVC by over 50% bitrate reduction on typical screen contents.

In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are (1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers), (2) Screen Content Fast Encoding Algorithms Design, and (3) Screen Content Fast Transcoding Algorithms Design.

Related Publications:

[J1] F. Duanmu, Z. Ma, and Y. Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2016 Aug; Vol: PP, Issue 99, Page:1-15. doi: 10.1109/JETCAS.2016.2597698

to:

To exploit the unique signal characteristics of screen content and develop efficient SC compression solutions, the ISO/IEC Moving Picture Expert Group and the ITU-T Video Coding Experts Group, also referred as the “Joint Collaborative Team on Video Coding” (JCTVC), has launched the standardization of SCC extension on top of the latest video standard - High Efficiency Video Coding (HEVC) since January 2014 and this extension is concluded in 2016 with significant research efforts involved from both academia and industry.

The official JCTVC Screen Content Model software (SCM) is reported to provide >50% BD-Rate saving over the HEVC Range Extension (RExt) for computer-generated contents. Novel coding tools and algorithms (e.g., palette coding mode, intra block copy, adaptive color transform, adaptive motion compensation precision, etc.) were introduced and adopted during the standardization.

In this project, we are exploring fast screen content encoding and transcoding solutions to accelerate screen content compression, while simultaneously preserving the rate-distortion performance, including:

(1) Fast Screen Content Encoding System Design using Machine Learning Techniques. (2) Fast HEVC-SCC Transcoding System Design using Machine Learning Techniques. (3) Fast SCC-HEVC Transcoding System Design using Statistical Mode Mapping Techniques.

Related Publications: (P: Patent; J: Journal; C: Conference) [J1] F. Duanmu, Z. Ma, M. Xu, and Y. Wang, “An HEVC-Compliant Fast Screen Content Transcoding Framework Based on Mode Mapping”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2018 (Under Review).

[J2] F. Duanmu, Z. Ma, and Y. Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2016 Aug; Vol: PP, Issue 99, Page:1-15. doi: 10.1109/JETCAS.2016.2597698

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[C3] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

[P1] “New SCC test sequence for consideration”, Doc. JCTVC-U0188, Warsaw, June, 2015.

[P2] “Non-CE1: Index Map Splitting (IMS) Mode for Palette Coding in HEVC SCC”, Doc. JCTVC-U0148, Warsaw, June, 2015.

[P3] “Non-CE1: Context model unification for palette run type flags in HEVC SCC”, Doc. JCTVC-U0149, Warsaw, June, 2015.

[P4] “Non-SCCE1: Analysis of Full Frame IBC Block Vector Distribution”, Doc. JCTVC-R0269, Sapporo, July, 2014.

[P5] “Non-SCCE3: Results of SCCE3 with support of 2x4 CTUs as reference buffer”, Doc. JCTVC-R0268, Sapporo, July, 2014.

[P6] “Non-SCCE3: Improvements for SCCE3 Test C3”, Doc. JCTVC-R0304, Sapporo, July, 2014.

to:

[C3] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015.

February 14, 2018, at 02:55 PM EST by 172.16.33.241 -
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Cloud based applications, such as virtual desktop interface, online gaming, shared screen collaboration, distance education, etc., have drawn more and more attention. Correspondingly, new opportunities and challenges are introduced on how to efficiently compress high-definition computer-generated screen content for limited bandwidth and computing resources. To better understand the natural statistics of screen contents and develop potential compression technologies, JCT-VC launched the development of screen content extension on top of HEVC in early 2014. Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model software (i.e., SCM) already outperforms conventional HEVC by over 40% bitrate reduction on typical screen contents.

to:

Screen content videos have become popular in recent years with the advances in mobile technologies and cloud applications, such as shared screen collaboration, remote desktop interfacing, cloud gaming, wireless display, animation streaming, online education, etc. These emerging applications create an urgent demand for better compression technologies and low-latency delivery solutions for screen content videos.

To exploit the unique signal characteristics of screen content and develop efficient SC compression solutions, the ISO/IEC Moving Picture Expert Group and the ITU-T Video Coding Experts Group, also referred as “Joint Collaborative Team on Video Coding” (JCTVC), has launched the standardization of SCC extension on top of the latest video standard - High Efficiency Video Coding (HEVC) since January 2014 and this extension is concluded in 2016 with significant research efforts involved from both academia and industry.

To better understand the natural statistics of screen contents and develop potential compression technologies, JCTVC launched the development of screen content extension on top of HEVC in early 2014. Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model software (i.e., SCM) already outperforms conventional HEVC by over 50% bitrate reduction on typical screen contents.

January 12, 2017, at 11:43 AM EST by 172.17.110.80 -
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In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are (1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers), (2) Screen Content Fast Encoding Algorithms Design, (3) Screen Content Fast Transcoding Algorithms Design, and (4) Screen Content Perceptual Coding Algorithm Design.

to:

In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are (1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers), (2) Screen Content Fast Encoding Algorithms Design, and (3) Screen Content Fast Transcoding Algorithms Design.

November 06, 2016, at 10:39 PM EST by 69.114.102.50 -
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In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are as follows:

(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers)

(2) Screen Content Fast Encoding Algorithms Design

(3) Screen Content Fast Transcoding Algorithms Design

(4) Screen Content Perceptual Coding Algorithm Design

to:

In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are (1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers), (2) Screen Content Fast Encoding Algorithms Design, (3) Screen Content Fast Transcoding Algorithms Design, and (4) Screen Content Perceptual Coding Algorithm Design.

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[P6] “Non-SCCE3: Improvements for SCCE3 Test C3”, Doc. JCTVC-R0304, Sapporo, July, 2014. [P7] “SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)”, Doc. JCTVC-R0248, Sapporo, July, 2014. [P8] “SCCE2: Cross-check of Line-based Intra Copy with 2-D BV and with Constrained PU-level Splitting (Test C)”, Doc. JCTVC-R0249, Sapporo, July, 2014. [P9] “SCCE3: Cross-Check of Test A.6 - Palette table generation (JCTVC-R0167)”, Doc. JCTVC-R0250, Sapporo, July, 2014. [P10] “SCCE3: Cross-check results of Test B.1 (JCTVC-R0121)”, Doc. JCTVC-R0251, Sapporo, July, 2014. [P11] “SCCE3: Cross-check results of Test B.2 (JCTVC-R0120)”, Doc. JCTVC-R0252, Sapporo, July, 2014. [P12] “SCCE4: Cross-check result of Test 3.1 (JCTVC-R0098)”, Doc. JCTVC-R0253, Sapporo, July, 2014. [P13] “SCCE5: Cross-check result of Test 3.2.2 (JCTVC-R0099)”, Doc. JCTVC-R0254, Sapporo, July, 2014. [P14] “Non-SCCE1: Cross-check of Additional results on intra block copy (IBC) (JCTVC-R0208)”, Doc. JCTVC-R0301, Sapporo, July, 2014

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[P6] “Non-SCCE3: Improvements for SCCE3 Test C3”, Doc. JCTVC-R0304, Sapporo, July, 2014.

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We propose an algorithm for separating the foreground(mainly text and line graphics) from the smoothly varying background in screen content images. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions,while the foreground text and graphics create sharp discontinuity and cannot be modeled by this smooth representation.The algorithm separates the background and foreground using a least absolute deviation method to fit the smooth model to the image pixels. This algorithm has been tested on several images from HEVC standard test sequences for screen content coding, and is shown to have superior performance over other popular methods, such as k-means clustering based segmentation in DjVu and shape primitive extraction and coding(SPEC) algorithm. Such background/foreground segmentation are important pre-processing steps for text extraction and separate coding of background and foreground for compression of screen content image.

to:

Cloud based applications, such as virtual desktop interface, online gaming, shared screen collaboration, distance education, etc., have drawn more and more attention. Correspondingly, new opportunities and challenges are introduced on how to efficiently compress high-definition computer-generated screen content for limited bandwidth and computing resources. To better understand the natural statistics of screen contents and develop potential compression technologies, JCT-VC launched the development of screen content extension on top of HEVC in early 2014. Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model software (i.e., SCM) already outperforms conventional HEVC by over 40% bitrate reduction on typical screen contents.

In this project, we are utilizing machine learning techniques and statistical studies to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the Rate-Distortion performance. The sub-areas investigated or being investigated are as follows:

(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers) (2) Screen Content Fast Encoding Algorithms Design (3) Screen Content Fast Transcoding Algorithms Design (4) Screen Content Perceptual Coding Algorithm Design

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Shervin Minaee and Yao Wang, "Screen Content Image Segmentation Using Least Absolute Deviation Fitting", International Conference on Image Processing, IEEE, 2015.

Shervin Minaee, AmirAli Abdolrashidi and Yao Wang, "Screen Content Image Segmentation Using Sparse-Smooth Decomposition", Asilomar Conference on Signals,Systems, and Computers, IEEE, 2015 (Selected as best paper award finalist)

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[J1] F. Duanmu, Z. Ma, and Y. Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2016 Aug; Vol: PP, Issue 99, Page:1-15. doi: 10.1109/JETCAS.2016.2597698 [C1] F. Duanmu, Z. Ma, and Y. Wang “A Novel Screen Content Fast Transcoding Framework Based on Statistical Study and Machine Learning”, in Proc. International Conference of Image Processing (ICIP), pp. 4205-4209, Phoenix, Arizona, USA, 2016. [C2] F. Duanmu, Z. Ma, and Y. Wang, “Fast CU partition decision using machine learning for screen content compression”, Proc. IEEE International Conference on Image Processing (ICIP), pp. 4972 - 4976, Quebec City, Canada, 2015. [C3] Y. Xu, W. Huang, W. Wang, F. Duanmu, and Z. Ma “2-D Index Map Coding for HEVC Screen Content Expression”, Proc. Data Compression Conference (DCC), pp. 263-272, Snowbird, Utah, USA, 2015. [P1] “New SCC test sequence for consideration”, Doc. JCTVC-U0188, Warsaw, June, 2015. [P2] “Non-CE1: Index Map Splitting (IMS) Mode for Palette Coding in HEVC SCC”, Doc. JCTVC-U0148, Warsaw, June, 2015. [P3] “Non-CE1: Context model unification for palette run type flags in HEVC SCC”, Doc. JCTVC-U0149, Warsaw, June, 2015. [P4] “Non-SCCE1: Analysis of Full Frame IBC Block Vector Distribution”, Doc. JCTVC-R0269, Sapporo, July, 2014. [P5] “Non-SCCE3: Results of SCCE3 with support of 2x4 CTUs as reference buffer”, Doc. JCTVC-R0268, Sapporo, July, 2014. [P6] “Non-SCCE3: Improvements for SCCE3 Test C3”, Doc. JCTVC-R0304, Sapporo, July, 2014. [P7] “SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)”, Doc. JCTVC-R0248, Sapporo, July, 2014. [P8] “SCCE2: Cross-check of Line-based Intra Copy with 2-D BV and with Constrained PU-level Splitting (Test C)”, Doc. JCTVC-R0249, Sapporo, July, 2014. [P9] “SCCE3: Cross-Check of Test A.6 - Palette table generation (JCTVC-R0167)”, Doc. JCTVC-R0250, Sapporo, July, 2014. [P10] “SCCE3: Cross-check results of Test B.1 (JCTVC-R0121)”, Doc. JCTVC-R0251, Sapporo, July, 2014. [P11] “SCCE3: Cross-check results of Test B.2 (JCTVC-R0120)”, Doc. JCTVC-R0252, Sapporo, July, 2014. [P12] “SCCE4: Cross-check result of Test 3.1 (JCTVC-R0098)”, Doc. JCTVC-R0253, Sapporo, July, 2014. [P13] “SCCE5: Cross-check result of Test 3.2.2 (JCTVC-R0099)”, Doc. JCTVC-R0254, Sapporo, July, 2014. [P14] “Non-SCCE1: Cross-check of Additional results on intra block copy (IBC) (JCTVC-R0208)”, Doc. JCTVC-R0301, Sapporo, July, 2014

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(2) Screen Content Fast Encoding Algorithm Designs
(3) Screen Content Fast Transcoding Algorithm Designs

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(2) Screen Content Fast Encoding Algorithm Design
(3) Screen Content Fast Transcoding Algorithm Design

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(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers):
(2) Screen Content Fast Encoding Algorithm:
(3) Screen Content Fast Transcoding Algorithm:
(4) Screen Content Perceptual Coding Algorithm:

to:

(1) Screen Content Coding Standardization Contributions
(2) Screen Content Fast Encoding Algorithm Designs
(3) Screen Content Fast Transcoding Algorithm Designs
(4) Screen Content Perceptual Coding Algorithm Design

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In this project, we are working on the following sub-areas.

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In this project, we are addressing the following sub-areas.

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Fanyi Duanmu from New York University

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Mr. Fanyi Duanmu from New York University

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In this project, we are working on the following sub-areas.

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In this project, we are working on the following sub-areas.

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Cloud based applications, such as virtual desktop interface, online gaming, shared screen collaboration, distance education, etc., have drawn more and more attention. Correspondingly, new opportunities and challenges are introduced on how to efficiently compress high-definition computer-generated screen content for limited bandwidth and computing resources. To better understand the natural statistics of screen contents and develop potential compression technologies, JCT-VC launched the development of screen content extension on top of HEVC in early 2014.

Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM) may outperform conventional HEVC by over 40% bitrate reduction on typical screen contents.

to:

Cloud based applications, such as virtual desktop interface, online gaming, shared screen collaboration, distance education, etc., have drawn more and more attention. Correspondingly, new opportunities and challenges are introduced on how to efficiently compress high-definition computer-generated screen content for limited bandwidth and computing resources. To better understand the natural statistics of screen contents and develop potential compression technologies, JCT-VC launched the development of screen content extension on top of HEVC in early 2014. Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model software (i.e., SCM) already outperforms conventional HEVC by over 40% bitrate reduction on typical screen contents.

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(2) Screen Content Fast Encoding Algorithms Design:
(3) Screen Content Fast Transcoding Algorithms Design:
(4) Screen Content Perceptual Coding Algorithm Design:

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(2) Screen Content Fast Encoding Algorithm:
(3) Screen Content Fast Transcoding Algorithm:
(4) Screen Content Perceptual Coding Algorithm:

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(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers) (2) Screen Content Fast Encoding Algorithms Design (3) Screen Content Fast Transcoding Algorithms Design (4) Screen Content Perceptual Coding Algorithm Design

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(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers):
(2) Screen Content Fast Encoding Algorithms Design:
(3) Screen Content Fast Transcoding Algorithms Design:
(4) Screen Content Perceptual Coding Algorithm Design:

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JCTVC Standard Proposals:

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Pending Patent:
84575021US01 - ADVANCED CODING TECHNIQUES FOR HEVC-SCC EXTENSIONS

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Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM-4.0) may outperform conventional HEVC by over 40% bitrate reduction on typical screen contents.

to:

Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM) may outperform conventional HEVC by over 40% bitrate reduction on typical screen contents.

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(1) Screen Content Coding Standardization (2) Screen Content Fast Encoder Design (3) Screen Content Fast Transcoder Design

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(1) Screen Content Coding Standardization Contributions (collaborating with Huawei researchers) (2) Screen Content Fast Encoding Algorithms Design (3) Screen Content Fast Transcoding Algorithms Design (4) Screen Content Perceptual Coding Algorithm Design

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Mr. Wei Wang from FutureWei Technologies Inc.

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Mr. Wei Wang from FutureWei Technologies Inc.
Mr. Shervin Minaee from New York University

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[JCTVC-R0248] SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)

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[JCTVC-R0248] SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)
JCTVC Standard Proposals:

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Contributed Standard Proposals:

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JCTVC Standard Proposals:

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Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM-4.0) may outperform conventional HEVC by over 35% bitrate reduction on typical screen contents. However, enormous computational complexity is introduced on encoder primarily due to heavy optimization processing, especially rate-distortion optimization (RDO) for Coding Unit (CU) partition decision and mode selection.

In this project, we are utilizing machine learning techniques and statistical study to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the RD performance.

to:

Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM-4.0) may outperform conventional HEVC by over 40% bitrate reduction on typical screen contents.

In this project, we are working on the following sub-areas. (1) Screen Content Coding Standardization (2) Screen Content Fast Encoder Design (3) Screen Content Fast Transcoder Design

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We also collaborate with researchers from Huawei on HEVC-SCC standardization with the following standardization proposals.
Related Standard Contribution:

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Contributed Standard Proposals:

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We also collaborate with researchers from Huawei on HEVC-SCC standardization with the following standardization proposals.

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We also collaborate with researchers from Huawei on HEVC-SCC standardization with the following standardization proposals.

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Dr. Meng Xu from Real Communications Inc.
Mr. Wei Wang from FutureWei Technologies Inc.

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We also collaborate with researchers from Huawei on HEVC-SCC standardization with the following standardization proposals.

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[JCTVC-R0248] SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)

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[JCTVC-R0248] SCCE1: Cross-check results of Test 3.4 (JCTVC-R0061)

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In this project, we are utilizing machine learning techniques and statistical study to develop fast encoding algorithms to reduce encoder complexity. Our proposed framework can significantly improve the encoding speed while simultaneously preserve the RD performance.

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In this project, we are utilizing machine learning techniques and statistical study to develop fast encoding and transcoding solutions to significantly reduce encoder / transcoder complexity, while simultaneously preserve the RD performance.

May 13, 2016, at 12:22 AM EST by 108.54.227.206 -
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May 13, 2016, at 12:17 AM EST by 108.54.227.206 -
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Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM-4.0) may outperform conventional HEVC by over 35% bitrate reduction on typical screen contents. However, enormous computational complexity is introduced on encoder primarily due to heavy optimization processing, especially rate distortion optimization (RDO) for Coding Unit (CU) partition decision and mode selection.

In this project, we are conducting research on two major directions:

(1) Standardization Direction: new coding algorithms to improve SCC coding efficiency and encoding throughput.

(2) Product Direction: fast encoding schemes and algorithms to boost encoder speed and reduce encoder memory and complexity consumption.

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Many new coding tools and algorithms (e.g., palette coding mode, intra block copy mode, adaptive color transform, etc.) were introduced during the standardization. The current testing model (a.k.a. SCM-4.0) may outperform conventional HEVC by over 35% bitrate reduction on typical screen contents. However, enormous computational complexity is introduced on encoder primarily due to heavy optimization processing, especially rate-distortion optimization (RDO) for Coding Unit (CU) partition decision and mode selection.

In this project, we are utilizing machine learning techniques and statistical study to develop fast encoding algorithms to reduce encoder complexity. Our proposed framework can significantly improve the encoding speed while simultaneously preserve the RD performance.

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Fanyi Duanmu
Prof. Yao Wang
Dr. Zhan Ma

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Fanyi Duanmu from New York University
Prof. Yao Wang from New York University
Dr. Zhan Ma from Nanjing University

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May 12, 2016, at 11:56 PM EST by 108.54.227.206 -
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May 12, 2016, at 11:55 PM EST by 108.54.227.206 -
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March 28, 2015, at 06:31 PM EST by Fanyi -
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In this project, we are investigating on two directions:

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In this project, we are conducting research on two major directions:

March 28, 2015, at 06:30 PM EST by Fanyi -
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In this project, we are conducting a series of statistical studies on screen contents and developing new SCC fast encoder schemes using machine learning techniques and new fast encoder algorithms.

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In this project, we are investigating on two directions:

(1) Standardization Direction: new coding algorithms to improve SCC coding efficiency and encoding throughput.

(2) Product Direction: fast encoding schemes and algorithms to boost encoder speed and reduce encoder memory and complexity consumption.

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March 28, 2015, at 06:24 PM EST by Fanyi -
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Fanyi Duanmu Prof. Yao Wang Dr. Zhan Ma

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Fanyi Duanmu
Prof. Yao Wang
Dr. Zhan Ma

March 28, 2015, at 06:23 PM EST by Fanyi -
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Page last modified on February 14, 2018, at 03:10 PM EST