Video coding standards k. R. Rao, Do Nyeon Kim J. J. Hwang Springer 2014
TS22 http://www3.americanradiology.com/pls/web1/wwimggal.vmg
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- IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS)
TS22 http://www3.americanradiology.com/pls/web1/wwimggal.vmgTS23 K. Seshadrinathan, A. C. Bovik, and L. K. Cormack. (2010). LIVE Video Quality Database. [Online]. Available: http://live.ece.utexas.edu/ research/quality/live_video.html
1. UT-Austin LIVE database is the most comprehensive database for IQA that includes 779 distorted images generated from 29 natural images. The distortions are JPEG2000, JPEG compression, white noise, Gaussian blur and Fast Fading Rayleigh Channel. URL: http://live.ece.utexas.edu/research/Quality/
2. The IVC database contains 10 original images and 235 distorted images generated from 4 different processing including JPEG, JPEG2000,LAR coding and Blurring. URL: http://www.irccyn.ec-nantes.fr/ivcdb/
3. The Toyama database includes 182 JPEG and JPEG2000 compressed images. URL: http://www.irccyn.ecnantes.fr/ touranch/ToyamaDatabase.rar
4. The Cornell-VCL A57 database has 60 distorted images with 6 types of distortions including 1) FLT:quantization of the LH subbands of a 5-level DWT of the image using the 9/7 filters, where the bands were quantized via uniform scalar quantization with step sizes chosen such that the RMS contrast of the distortions was equal; 2) NOZ: additive Gaussian white noise; 3) JPG: baseline JPEG compression; 4) JP2: JPEG2000 compression using the 9/7 wavelet and no visual frequency weighting; 5) DCQ: JPEG2000 compression using the 9/7 wavelet with the dynamic contrast-based quantization algorithm, which applies greater quantization to the fine spatial scales relative to the coarse scales in an attempt to preserve global precedence; 6) BLT: blurring by using a Gaussian filter. URL: http://foulard.ece.cornell.edu/
VQA database: The VQEG phase 1 test database is the only publicly-accessible and the most extensively used database with DMOS for video quality assessment. The video database includes 20 SDTV reference video sequences and 320 decoded video sequences. URL: http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html
TS24 S. Péchard, R. Pépion, and P. Le Callet. IRCCyN/IVC Videos 1080i Database. [Online]. Available: http://www.irccyn.ec-nantes. fr/~lecallet/platforms.htm, accessed Oct. 16, 2015. TS25 F. Zhang, S. Li, L. Ma, Y. C. Wong, and K. N. Ngan. IVP Subjective Quality Video Database. [Online]. Available: http://ivp.ee.cuhk.edu.hk/research/databas/subjective/, accessed Oct. 16, 2015. TS26 J.-S. Lee et al. (2010). MMSP Scalable Video Database. [Online].
Screen content video has evolved from a niche to a mainstream due to the rapid advances in mobile and cloud technologies. Real-time, low-latency transport of screen visuals between devices in the form of screen content video is becoming prevalent in many applications, e.g. wireless display, screen mirroring, mobile or external display interfacing, screen/desktop virtualization and cloud gaming. Today's commonly-used video coding methods, however, have been developed primarily with camera-captured content in mind. These new applications create an urgent need for efficient coding of screen content video, especially as the support of 4k or even 8k resolution begins to achieve mass market appeal. Screen content video coding poses numerous challenges. Such content usually features a mix of computer generated graphics, text, and camera-captured images/video. With their distinct signal characteristics, content adaptive coding becomes necessary. This is without mentioning the varied level of the human's visual sensitivity to distortion in different types of content; visually or mathematically lossless quality may be required for all or part of the video. Recognizing the demand for an industry standard for coding of screen content, the ISO/IEC Moving Picture Experts Group and ITU-T Video Coding Experts Group have since January 2014 been developing new extensions for HEVC. The Video Electronics Standards Association also recently completed a Display Stream Compression (DSC) standard for next-generation mobile or TV/Computer display interfaces. The development of these standards introduced many new ideas, which are expected to inspire more future innovations and benefit the varied usage of screen content coding. Besides coding, there are many other challenging aspects related to screen content video. For instance, in applications like screen mirroring and screen/desktop virtualization, low-latency video processing and transmission are essential to ensure an immediate screen response. In addition to real-time streaming technologies, these applications need a parallel-friendly screen encoding algorithm that can be performed efficiently on modern mobile devices or remote servers in the data center, and require, in certain use cases, the harmony of their computing resources, to keep the processing time to a minimum. At the receiver side, best-effort decoding with consideration for transmission errors, along with visual quality enhancement, is expected. Addressing these constraints requires research from multiple disciplines as is the case for other applications. The intent of this special issue is to present the latest developments in standards, algorithms, and system implementations related to the coding and processing of screen content video. Original and unpublished research results with topics in any of the following areas or beyond are hereby solicited. - Screen content video coding techniques and standards, e.g. HEVC extensions and DSC - Visually or mathematically lossless screen content video coding - Application-specific screen content coding, e.g. display stream or frame memory compression - Screen-content related pre/post-processing, e.g. resizing and post-filtering - Visual quality assessment for screen content video - Parallel-friendly, low-delay encoding optimization - Robust decoding with error and power control - Hardware/software/cloud-based screen codec implementations - Real-time, adaptive screen content transport over Internet or wireless networks - Design examples of novel screen content video applications, e.g. screen/desktop virtualization and cloud gaming - System performance analysis and characterization Important dates - Manuscript submissions due 2016-01-22 - First round of reviews completed 2016-03-25 - Revised manuscripts due 2016-05-13 - Second round of reviews completed 2016-07-08 - Final manuscripts due 2016-07-22
Wen-Hsiao Peng wpeng@cs.nctu.edu.tw National Chiao Tung University, Taiwan Ji-Zheng Xu jzxu@microsoft.com Microsoft Research Asia, China Jöern Ostermann ostermann@tnt.uni-hannover.de Leibniz Universität Hannover, Germany Robert Cohen cohen@merl.com Mitsubishi Electric Research Laboratories, USA S. – H. Tsang, Y. – L. Chan and W. – C. Siu, “Fast and Efficient Intra Coding Techniques for Smooth Regions in Screen Content Coding Based on Boundary Prediction Samples”, ICASSP2015, Brisbane, Australia, April. 2015. J. Nam, D. Sim and I.V. Bajic, “HEVC-based Adaptive Quantization for Screen Content Videos,” IEEE Int. Symposium on Broadband Multimedia Systems, pp. 1-4, Seoul, Korea, 2012.
HM-16.9 Software -> https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.9/ HM-16.9 Software Manual -> https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.9/doc/software-manual.pdf ElecardHEVCAnalyser: http://www.elecard.com/en/products/professional/analysis/hevc-analyzer.html Scalable Extension of HEVC -> https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware Encoding time evaluation: Intel VTune AmplIfier XE Software profiler, Available http://software.intel.com (accessed May 6, 2014) See reference 31 in [E242] Yüklə 0,84 Mb. Dostları ilə paylaş:
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