Direction Adaptive Wavelet Based Image Processing Algorithm And Geometric Feature Preserving Quality Assessment

With the development of technology and the increase of requirement, it is becoming more and more difficult to obtain visually satisfactory result by conventional image processing algorithms. The limitations of commonly used separable extensions from one-dimensional transforms for images, such as discrete wavelet transform (DWT), are well known; e.g. these separable transforms cannot take advantage of intrinsic geometrical structure in high dimensional signals and are not the optimal algorithms for image. To improve the performance of image processing algorithms, it is necessary to explore the geometry in image and the characteristic of human visual system which are desirable features for many consumers and practical applications. That is to say, the image processing algorithms should be directly driven by the structure of image data.The performance of image processing algorithms closely relies on the accuracy of employed models to characterize the image. However, it is difficult to construct an accurate image model in according to visual prediction and practical applications. The main challenge in exploring geometry in images comes from the discrete nature of image data and complexity of image structures. In this thesis, we study the characteristic of image data and human visual system and solve some crucial issues in algorithms to overcome the disadvantage of traditional image compression, image resolution enhancement, and image quality assessment. The main contributions of this thesis could be summarized as follows:1. In order to overcome the disadvantage of conventional wavelet based image compression, we propose an edge-directed orthogonal wavelet transform which is driven from the geometric structural feature of image. The proposed method inherits the advantage of wavelet and explores the directional features of images. Different schemes are implemented based on the property of image blocks to reduce the computational complexity. Experiments show that the new method can protect effectively the geometric feature which plays an important role in visual perception. Meanwhile, the extension of this method to wavelet packets for SAR image compression is straightforward.2. The blur and jaggy of image details or edges are inevitable during conventional image interpolation. In order to obtain interpolated images with better quality, we propose wavelet based edge-preserving direction adaptive image interpolation method. We apply the improved bilinear interpolation method with adaptive direction to interpolated image. Wavelet is implemented to provide more high frequency information, and post-processing is applied to improve the visual quality of interpolated images. The experimental results show that our method can achieve interpolated image with high quality, both subjectively and objectively. 3. A novel single image super-resolution reconstruction algorithm is proposed based on the geometrical model on the phase and amplitude of dual-tree complex wavelet coefficients of the image. The dual-tree complex wavelet has the properties of approximate shift-invariance and flexible directionality, and can achieve sparser image representation compared with standard wavelet. The appropriate geometric regularization is designed based on the priors of the amplitude and phase of complex wavelet coefficients for super resolution image reconstruction. Then, Split Bregman iteration is utilized in our proposed approach for optimization to gain high quality super resolution image.4. Inspired by the researches of quality prediction of human visual system and the intrinsically geometric structural features of natural images, a novel geometric structural distortion model based full reference image quality assessment method is proposed to overcome the deficiencies in traditional methods. Basically there are three components in our measurement to characterize the geometric structural distortion: direction, magnitude and sharpness. The proposed measurement fits the physical observations for various image distortions and has relatively low computational complexity. The experimental results on image database show that the performance of our method is consistent with the subjective assessment of human beings. Meanwhile, wavelet based geometric structural distortion is proposed in according with perceptual property of human eye, where wavelet transform is used because it matches well the multi-channel model of HVS. The experimental results demonstrate the advantage of proposed model.5. A novel reduced reference image quality assessment based on natural image statistical prior in gradient domain is proposed. The research in human visual system shows that edge information plays an important role in visual perception. On the other hand, it obeys a specify distribution for natural image, where some statistical features of reference image are extracted and sent to receiver side. The distortion measure for distorted image is defined with comparison of these features. The experimental resuts on standard image database shows that proposed method is general purposed for all distortion types.In summary, following the characteristic of image data and human visual perception, this thesis provides several systemic researches about the problem of direction adaptive wavelet based image processing algorithms and geometric structural features preserving image quality assessment.