Wavelet transform image coding based on fuzzy visual perception modeling

Abstract

In this paper, we propose to incorporate both spatial and frequency models of HVS into wavelet transform image coding. The process of Wavelet transform decomposition, which splits the spatial frequency domain to several octave bands by dilation and translation of a single basic wavelet, is similar to that of frequency model HVS. Moreover, according to spatial model of HVS, some compact physical features like contours and regions are with highly visually significant to human vision system. Based on the spatial model, we apply fuzzy logic theory to detect visual significant edge points and based on these edge points to construct a Visual Perception Sensitive Map (VPSM) for wavelet coefficient thresholding scheme. Only the visual significant coefficients are retained and the rest are discard. This approach can achieve a high image compression ratio while minimizing the visual quality distortion of the reconstructed image. In addition, we develop an adaptive quantization scheme for the wavelet coefficients at each of the subbands. This quantization scheme is developed based on the HVS frequency model to minimize the visual errors caused by the quantization.In our image compression system, both the frequency and spatial aspects of HVS to the image have been taken into consideration. We preserve the highly visual perceptive wavelet coefficients and minimize the visual distortion of coefficients in each of the decomposed band. As a result, a high compression ratio with low visual distortion coder is obtained.

Multiresolution View Morphing in the Wavelet Domain

“…Compared with field image morphing, wavelet-based image morphing can accelerate the morphing process because wavelet analysis can be viewed as a sub-sampling of the original image, and it is known that field morphing algorithms depend on the resolution and the number of feature lines. Also, since the DWT transforms images into the frequency domain, some frequency-based analysis can be embedded, such as quantization [9], thresholding [11], and noise removal. An interesting effect that cannot be obtained from the spatial image domain based morphing method is mentioned in [3]: changing the transition rate of different frequency bands.…”

“…This feature is very suitable for distributed and networked image and video applications, especially for distributed and Internet IBMR applications. Second, with the rich and effective wavelet-based image compression techniques [9,11], a compression scheme can be embedded in a wavelet domain, such as VQ. This would reduce a large number of data transported and the decoding and rendering can be performed at the receiver side.…”

“…Corresponding coefficients between two decomposed images are linearly interpolated to form the multiresolution representation of an intermediate view. Quantization techniques [10,11] can be embedded here to compress the coefficients for the purpose of reducing the morphing complexity. Finally, during the display, compressed images are decoded and an inverse wavelet transform is applied.…”

Multispectral Image Processing: The Nature Factor

“…10,30 Part of the problem relates to how we perceive objects based on texture and color variations and how these processes can be applied to imagery in spectral bands outside of the visible. 2,4,25 Finally, neural nets and fuzzy logic are beginning to be used as the image processing speed available in personal computers has attained the hundreds of MHz central processing unit clock speed. 11 How is the Nature Factor used?…”