A Survey of Parallel Algorithms for Fractal Image Compression <s> Related parallel algorithms <s> Iterated function systems (IFSs) have received great attention in encoding and decoding fractal images. Barnsley (1988) has shown that IFSs for image compression can achieve a very high compression ratio for a single image. However, the major drawback of such a technique is the large computation load required to both encode and decode a fractal image. We provide a novel algorithm to decode IFS codes. The main features of this algorithm are that it is very suitable for parallel implementation and has no transient behavior. Also, from the decoding process of this method we can understand the encoding procedure explicitly. One example is illustrated to demonstrate the quality of its performance. <s> BIB001 </s> A Survey of Parallel Algorithms for Fractal Image Compression <s> Related parallel algorithms <s> Since fractal image compression is computationally very expensive, speedup techniques are required in addition to parallel processing in order to compress large images in reasonable time. In this paper we introduce a new parallelization approach for fractal image compression algorithms which employ block classification as speedup method suited for multicomputers. <s> BIB002 </s> A Survey of Parallel Algorithms for Fractal Image Compression <s> Related parallel algorithms <s> Photonics and Information LaboratoryDepartment of Electrical EngineeringNational Yunlin University of Science andTechnologyDouliu Yunlin, 640 Taiwan ROCChung J. KuoMagnetics and Microwave Business UnitsComponents Business GroupDelta Electronics, Inc.Taoyuan, 333 Taiwan ROCAbstract. A parallelized random iteration algorithm that is explicit in theiterated function system (IFS) code is proposed to speed up the decod-ing of fractal images. In a conventional random iteration algorithm, thefractal image is serially decoded and may generate undesirable transientpoints. The proposed intrinsic parallel random iteration (IPRI) algorithmdetermines only one initial point and can avoid the transient points.Moreover, the contractive afﬁne transformations (CATs) denoted by theIFS code can operate in parallel and simultaneously generate subim-ages. The parallel capability is basically proportional to the number ofCATs. Two generalized methods of the proposed IPRI algorithm are pro-vided to solve the nonuniformity problem due to the unequal probabilitiesamong CATs. Finally, the proposed IPRI algorithm can cooperate with aprevious parallel decoding algorithm to obtain higher parallel capability.Simulation results demonstrate the validity of the proposed IPRI algo-rithm. <s> BIB003
Although this short survey has focused primarily on parallel algorithms for fractal image compression, it should be noted that many of the techniques discussed extend directly to the compression of video. This is well demonstrated in BIB002 for example, where a pool of three-dimensional range blocks (referred to as "range cubes") is created by considering sequences of video images together. For example, a sequence of 16 images of 512 × 512 pixels may be broken down into 8192 range cubes of size 8 × 8 × 8 pixels. The parallel algorithms then partitions these range cubes across the available processors. Although less important than parallel image and video compression, it is also relevant to note at this point that a number of parallel algorithms have been proposed for fractal image decoding too. Examples include BIB003  BIB001 although these are beyond the scope of this particular paper. Clearly the benefits from parallel fractal image decoding are not likely to be so great as with the coding algorithm, since the decoding algorithm is so much faster in the first place: this is one of the main attractions of fractal image compression after all.