Open Access
Low Complexity H.264 Encoder Using Machine Learning For Streaming Applications
Suchethan Swaroop Vaidyanath
- 14 Jul 2011
TL;DR: These two softwares are compared in terms of execution time and video quality of the decoded sequences and the compression ratio of H.264 file is found to be less in JM software at various bit rates than in Intel IPP.
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Abstract: LOW COMPLEXITY H.264 ENCODER USING MACHINE LEARNING FOR STREAMING APPLICATIONS Suchethan Swaroop Vaidyanath, M.S The University of Texas at Arlington, 2011 Supervising Professor: K.R.Rao H.264, MPEG-4 part-10 or AVC, is the latest digital video codec standard which has proven to be superior than earlier standards in terms of compression ratio, quality, bit rates and error resilience [1]. Joint model (JM) reference software is used for academic reference and it was developed by the Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG (Video coding experts group). The Intel IPP H.264 (Integrated Performance Primitives) is a product of Intel which uses Intel IPP libraries and SIMD instructions available on modern processors. The Intel IPP H.264 is multithreaded and uses CPU optimized IPP routines. These two softwares are compared in terms of execution time and video quality of the decoded sequences. The metrics used for comparison are SSIM (Structural Similarity Index Metric), PSNR (Peak-to-Peak Signal to Noise Ratio), MSE (Mean Square Error), motion estimation time, encoding time, decoding time and the compression ratio of the H.264 file size (encoded output). The compression ratio of H.264 file is found to be less in JM software at various bit rates than in Intel IPP. Hence, it is preferred over Intel IPP for reduction in the
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References
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
Overview of the H.264/AVC video coding standard
TL;DR: An overview of the technical features of H.264/AVC is provided, profiles and applications for the standard are described, and the history of the standardization process is outlined.
•Book
H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia
Iain E. G. Richardson
- 19 Dec 2003
TL;DR: In this article, the MPEG-4 and H.264 standards are discussed and an overview of the technologies involved in their development is presented. But the focus is on the performance and not the technical aspects.
2.5K
H.264 and MPEG-4 Video Compression
Iain E. G. Richardson
- 02 Sep 2003
TL;DR: This paper presents a meta-review of the MPEG-4 and H.264 standards for video quality and design, and some of the standards themselves have been revised and improved since their publication in 2009.
2.3K