This paper presents a modified orthogonal fractal super-resolution (OFSR) method to improve the visual quality of an image along sharp edges. Although the OFSR method constructs a high-quality high-resolution image from a low-resolution counterpart, there are ringing artifacts observed along sharp edges which make the visual quality relatively low with respect to the numerical quantity. These artifacts are mainly caused by unnecessarily exaggerated pixel contrast along sharp edges within a range block. We restrict each contracted pixel value in a range block to a value between the minimum and maximum of its domain block pixel values. We also extend the domain block of the contraction function and find a better domain block using the range block mean. At the final step of the iteration, we adjust each pixel in the range block so that the range block mean and the corresponding pixel value of the low-resolution image are equal. According to our experimental results, the proposed method improves the visual quality along sharp edges and shows higher levels of numerical quantity than the OFSR method.

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

We present an efficient scheme for progressive fractal coding in a wavelet multiresolution pyramid. In our proposed scheme, range blocks are selectively coded, based on energy localization in wavelet bands. In order to form domain pools with blocks yielding less distortion, domain blocks are also selectively searched in the wavelet bands of either the same or a different orientation from that of each range block at the next lower resolution. It is shown that the performance of the non-hybrid fractal coder presented is much better than that of predictive pyramid coding (PPC).

A new algorithm is presented for recovering the blocks lost with the cell loss in the ATM transmission of the images coded by Jacquin's fractal coding. The key technique of the proposed BLRA (block loss recovery algorithm) is a fractal extrapolation that estimates the pixels in a lost block by using the contractive mapping parameters of a range block homogeneous to the lost block. The proposed BLRA is applied to the lost blocks in the iteration of decoding.