Image Compression: A Survey <s> RESULTS AND DISCUSSION <s> Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB. > <s> BIB001 </s> Image Compression: A Survey <s> RESULTS AND DISCUSSION <s> A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes. <s> BIB002 </s> Image Compression: A Survey <s> RESULTS AND DISCUSSION <s> In this paper, a new similarity measure for fractal image compression (FIC) is introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. When the original image is corrupted by noises, we argue that the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression. The proposed HFIC is one of our attempts toward the design of robust fractal image compression. The main disadvantage of HFIC is the high computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed HFIC is robust against outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image. <s> BIB003
Fractal image compression: At first Fractal Image Compression (FIC) was recommended but major drawback of FIC was poor retrieved image quality when compression is apply on noisy or corrupted images. So, to overcome this limitation, HFIC is proposed in which contrast and brightness estimate through M-estimation. The main drawback is high computational cost which is solving through PSO which reduce time needed for searching. This technique is efficient because it retain the quality of image BIB003 . Figure 2 shows some Fractal Image Compression Techniques. Region-based heuristic search compression: By using different transformations classes a novel algorithm that enlarges the block-based scheme. Implemented methods are not block based of fixed size square. It is region based large and irregular shaped segments of images can be used as regions. These region-based transformations used to encode images. Heuristic algorithms used to construct these transformations BIB001  . Nearest neighbor search is based on pre- quantization and orthogonal projection which are parameters of fractal transform. It is shown from results that our new technique has the ability to enhance both rate of compression and fidelity thus encoding time and memory demand minimizes BIB002 . A method for exploiting the time redundancy of region borders is proposed. The additional information improves the signal quality produced by the classified region coding scheme. The predictor used for image sequence coding is very simple. In order to achieve better results more complex predictor based on motion estimation and compensation  .