Application of Embedded Wavelet Image Coding Algorithm in Track and Field Route Optimization

2013 ◽  
Vol 791-793 ◽  
pp. 1166-1171
Author(s):  
Tao Li
Keyword(s):  
Image Fusion ◽  
Frequency Domain ◽  
Image Coding ◽  
Route Optimization ◽  
Human Beings ◽  
Fusion Model ◽  
Analog Simulation ◽  
Wavelet Image Coding ◽  
Wavelet Image ◽  
Wavelet Algorithm

Embedded wavelet image coding algorithm has excellent localization property in time domain and frequency domain. To some extent, it can fix position for graphic information which has different direction features to any precision level. At the same time, with the disappear of blocking effect and noise, it is able to perfectly match with the visual features of human beings and it has quickly become one of the hot research direction in the field of image processing. This paper firstly defines the wavelet transform and elaborates the principle and connotation of embedded wavelet algorithm. And then, this paper reconstructs the image wavelet. On the basis of this, wavelet algorithm is transformed in frequency domain. At the same time, this paper constructs the image fusion model which is based on embedded wavelet image coding algorithm and further applies the edge detection and image fusion of the model to the track route. Analog simulation is also made in the application of the algorithm and the effect of real virtual composite is obvious. To some extent, it provides new exploration ideas and practice path for the research in this field.

scholarly journals REGULARITY SCALABLE IMAGE CODING BASED ON WAVELET SINGULARITY DETECTION

2008 ◽  
Vol 08 (01) ◽  
pp. 109-134 ◽  
Author(s):  
CHARLOTTE YUK-FAN HO ◽  
TAI-CHIU HSUNG ◽  
DANIEL PAK-KONG LUN ◽  
BINGO WING-KUEN LING ◽  
PETER KWONG-SHUN TAM ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Computational Effort ◽  
Irregular Sampling ◽  
Scalable Coding ◽  
Signal To Noise ◽  
Reconstruction Process ◽  
Wavelet Image Coding ◽  
Wavelet Transform Modulus Maxima ◽  
Wavelet Image

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficiency.

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