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Low complexity H.264 video encoder design using machine learning techniques
| Title: | Low complexity H.264 video encoder design using machine learning techniques. |
 459 views 301 downloads |
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| Name(s): |
Carrillo, Paula. Department of Computer and Electrical Engineering and Computer Science College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Issued: | 2008 | |
| Publisher: | Florida Atlantic University | |
| Physical Form: | electronic | |
| Extent: | xiii, 59 p. : ill. (some col.). | |
| Language(s): | English | |
| Summary: | H.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This thesis applies machine learning technique for video encoding mode decisions and investigates ways to improve the process of generating more general low complexity H.264/AVC video encoders. The proposed H.264 encoding method decreases the complexity in the mode decision inside the Inter frames. Results show, at least, a 150% average reduction of complexity and, at most, 0.6 average increases in PSNR for different kinds of videos and formats. | |
| Identifier: | 316798878 (oclc), 166448 (digitool), FADT166448 (IID), fau:2831 (fedora) | |
| Note(s): |
by Paula Carrillo. Thesis (M.S.C.S.)--Florida Atlantic University, 2008. Includes bibliography. Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web. |
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| Subject(s): |
Code division multiple access Digital media -- Technological innovations Image transmission -- Technological innovations Coding theory Data structures (Computer science) |
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| Persistent Link to This Record: | http://purl.flvc.org/FAU/166448 | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
