To improve compression performance, and realize the automatic selection of compression algorithms in processing images without the prior information, the regularity relationship between compression algorithms and image features is studied, and a preprocessing scheme of block and classification based on image features has been proposed in this paper. Compression algorithms pre and post preprocessing scheme are investigated for the same 100 images. Our scheme achieves the larger peak signal to noise ratio (PSNR), which demonstrates the effectiveness of the proposed preprocessing scheme of block and classification in improving compression performance and selecting suitable algorithm to process image without the prior information.
Scene matching is used for image registration in many fields. There are usually translation and an arbitrary unknown rotation angle between reference and template images. The corresponding scene matching algorithm costs far more computing time than that with small rotation angle and translation. Conceptions of generalized vector image, gray-scale image rotation transformation, gray-scale image point transformation, nature of shift invariance and rotation invariance are proposed to form the foundation of this paper. Radial Projection Fourier Transform (RPFT) is proposed and its rotation invariance is formal proved in this paper. It is applied to the Algorithm of Scene Matching with Rotation Invariance (ASMRI).Calculation on reference and template images can be done separately. Some works can be done before the template images are required. This can improve the matching speed at the cost of more memory.A program to implement the proposed RPFT and ASMRI based on RPFT is coded by means of Visual C++ 6.0. The results prove that ASMRI based on RPFT is not only rotation invariant, but also more accurate and faster than traditional methods. The program can be carried out with hardware.
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