Recently, video has become one of the most important multimedia resources to be shared in our work and daily life. With the development of high frame rate video (HFV), the write speed from high speed camera array sensor to the massive data storage device has been regarded as the main constraints on HFV applications. In this thesis, some low-complexity compression techniques are proposed for HFV acquisition and transmission. The core technique of our developed codec is the application of Slepian-Wolf coding theorem in video compression. The light-duty encoder employs SW encoding, resulting in lower computational cost. The pixel values are transformed into bit sequences, and then we assemble the bits on same bit plane into 8 bit streams. For each bit plane, there is a statistical BSC being constructed to describe the dependency between the source image and the SI image. Furthermore, an improved coding scheme is applied to exploit the spatial correlation between two consecutive bit planes, which is able to reduce the source coding rates. Different from the encoder, the collaborative heavy-duty decoder shoulders the burden of realizing high reconstruction fidelity. Motion estimation and motion compensation employ the block-matching algorithm to predict the SI image. And then the received syndrome sequence is able to be SW decoded with SI. To realize different compression goals, compression are separated to the original and the downsampled cases. With regard to the compression at the original resolution, it completes after SW decoding. While with respect to compression at reduced resolution, the SW decoded image is necessary to be upsampled by the state-of-the-art learning based SR technique: A+ . Since there are some important image details lost after the resolution resizing, ME and MC is applied to modify the upsampled image again, promoting the reconstruction PSNR. Experimental results show that the proposed low-complexity compression techniques are effective on improving reconstruction fidelity and compression ratio. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22193 |
| Date | January 2017 |
| Creators | Yang, Duo |
| Contributors | Chen, Jun, Wu, Xiaolin, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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