Low complexity mosaicking and up-sampling techniques for high resolution video display

Several challenging issues for applications of image/video mosaicking and upsampling with high resolution are addressed here, all of which are mainly conducted in DCT (Discrete Cosine Transform) domain so that lower computation complexity can be achieved.;First of all, color matching and compensation techniques are proposed to remove the seam lines between image boundaries due to the different color tones of the inputs. Color deviation of each input image is corrected first and color differences between input images are then compensated using the polynomial-based contrast stretching technique. The proposed approach is attractive for its lower computational complexity. Experimental results demonstrate that the color-matching problem can be satisfactorily solved in the compressed domain even when the DCT blocks of original input images are not aligned.;Two block-level image registration techniques for compressed video such as motion JPEG or the I-picture of MPEG are investigated. The proposed methods are based on edge estimation and extraction in DCT domain so that the computational cost of image registration is reduced dramatically as compared with the pixel-domain edge-based registration techniques while achieving certain quality of composition. In order to reach higher accuracy of registration, a post-processing technique, hybrid block/pixel level alignment, is proposed so that the displacement vector resolution can be enhanced from the block level to the pixel level. As compared with the traditional spatial-domain processing, we do not perform the inverse DCT transform to the whole image but to some selected blocks. It is shown by experiments that the proposed algorithm saves around 40% of the computational complexity while achieving the same quality.;In the last part, a content adaptive technique is proposed to upsample an image to an output image of higher resolution. The proposed technique is also a block-based processing algorithm that offers the flexibility in choosing the most suitable up-sampling method for a particular block type. Block classification is first conducted in the DCT domain to categorize each image block into several types: smooth areas, textures, edges and others. For the plain background and smooth surfaces, simple patches are used to enlarge the image size without degrading the resultant visual quality. The unsharp masking method is applied to the textured region to preserve high frequency components. Since human eyes are more sensitive to edges, we adopt a more sophisticated technique to process edge blocks. That is, they are approximated by a facet model so that the image data at subpixel positions can be generated accordingly. A post-processing technique such as 1D directional unsharp masking can be used to enhance edge sharpness furthermore. Experimental results are given to demonstrate the efficiency of the proposed techniques.