Modelling Nonlinear Dynamic Textures using Hybrid DWT–DCT and Kernel PCA with GPU
| Autor: | N. B. Chopade, Premanand Ghadekar |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Graphics processing unit 020207 software engineering YCbCr Pattern recognition 02 engineering and technology Kernel principal component analysis Nonlinear system Computer Science::Graphics Computer Science::Computer Vision and Pattern Recognition Computer Science::Multimedia Compression ratio 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) Discrete cosine transform 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business Time complexity |
| Zdroj: | Journal of The Institution of Engineers (India): Series B. 97:549-555 |
| ISSN: | 2250-2114 2250-2106 |
| Popis: | Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT–DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT–DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism. |
| Databáze: | OpenAIRE |
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