The Research On Directional Transforms And Their Application In Remote Senscing Image Compression

With the development of remote sensing, it is becoming more and more difficult to compress remote sensing images with much higher resolution. The traditional transforms used in image compression, such as discrete wavelet transform (DWT), cannot take advantage of geometric regularity in high dimensional signals, which is a central issue to improve compression performance. So, it is critical to develop the transforms with directional selectivity that can represent the anisotropic edges and textures more sparsely. Because remote sensing data must be processed in real time in many cases, the construction of directional transforms with limited redundancy and their fast coding algorithms are the two main challenges.To get better sparsity, the nature of directions in dual tree (DT) multiscale transform and directional lifting scheme is deeply studied in this thesis. And new fast transforms with improved directional selectivity are constructed. Furthermore, the corresponding efficient codecs are designed and used in remote sensing images compression.The main contributions in this thesis are as follows.A new dual-tree structure is proposed, which can represent directional edges and textures in remoting sensing images better. (1) Firstly, the Hilbert transform pair of the high pass filters in DT is defined. Based on the definition, the directional selectivity of DT structure is proved, which is stricter than the explanation based on the analyticity of wavelet functions. (2) Secondly, considering that the filters in the first level DT are not a Hilbert transform pair, a modified DT structure that offers more directional information is proposed. (3) At last, a wavelet pair with better directional selectivity and higher vanishing moments is obtained, by constraining their high pass filters to form a Hilbert transform pair.A highly efficient codec of DT coefficients is designed based on the proposed DT structure. (1) A direction adaptive context model is developed to reveal the correlation left between adjacent coefficients. (2) The corresponding bands in different trees are proved to be uncorrelated. Using the uncorrelation, a rate-distortion optimization algorithm is proposed and used in DT based compression. Comparing with available DT based compression methods, peak signal-to-noise ratios (PSNR) of our method are 0.1-0.4 dB higher. (3) The influence of denoising on compression in the DT domain is investigated. And a closed-form probability density function (PDF) of noise in DT coefficients is deduced, which approximates the true distribution better comparing with other PDFs now used and gets about 0.3dB coding gain.A fast directional lifting biorthogonal lapped transform (LBT) is constructed to reduce the computation. (1) The directional mode without interpolation is given and used in directional lifting LBT. It can be performed as efficiently as LBT. (2) An algorithm based on tree pruning is proposed to optimize lifting directions globally. Comparing with the algorithm available, its computation decreases form O ( A3 N ) to O ( AN ), where A is the number of directions and N is the size of the image. (3) A set partition codec is used to code directional lifting LBT coefficients, and the compressed remote sensing images get higher PSNRs.Our directional transforms and coding algorithms are practical for the high speed and high fidelity transmission of remote sensing images. Some of them have been successfully used in space and aviation remote sensing.