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Content Provider | IET Digital Library |
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Author | Wang, Li Li Siu, Wan Chi |
Abstract | In this study, an efficient approach for detecting zero-quantised discrete cosine transform (DCT) coefficients for video coding is developed. Compared with conventional detection methods of zero-quantised DCT coefficients used in H.264/AVC, the proposed algorithm has two major features. First, a new classification of patterns for DCT, quantisation, inverse quantisation and inverse discrete cosine transform processes is proposed. By taking a zigzag scanning order into the classification, the quantised DCT coefficients can be coded efficiently. Second, the thresholds for detecting zero-quantised DCT coefficients are determined by combining the Gaussian distribution with a theoretical analysis of the DCT and quantisation in H.264/AVC. Experimental results show that the proposed algorithm achieves an average timesaving of more than 40% compared with the algorithm in reference software JM12.2 of H.264/AVC. When compared with other algorithms in the literature, it also gives the best performance in terms of both rate-distortion and time saving. |
Starting Page | 92 |
Ending Page | 97 |
Page Count | 6 |
ISSN | 17519659 |
Volume Number | 7 |
e-ISSN | 17519667 |
Issue Number | Issue 1, Feb (2013) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/7/1 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2011.0355 |
Journal | IET Image Processing |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Data Compression Discrete Cosine Transform Gaussian Distribution H.264/AVC Image And Video Coding Integral Transforms Inverse Discrete Cosine Transform Process Inverse Quantisation Inverse Transforms Pattern Classification Quantisation (signal) Rate Distortion Signal Classification Statistics Video Coding Video Signal Processing Zero-quantised Discrete Cosine Transform Coefficient Zigzag Scanning Order |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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