This research introduces a new concept of compression with a different approach on lossless image compression that has been commonly known. The concept uses Reversible Contrast Mapping (RCM) which is a function of a simple integer transform developed for reversible watermarking method. The use of RCM in image compression is done similarly with the watermarking method. In this method, an image is divided into fixed-size blocks, the blocks are divided into two groups based on the data storage capacity. Blocks with smaller storage capacities are used as watermarks for the other blocks. The compression ratio of this method is similar to the Huffman compression. This method can also be used together with the Huffman compression technique to increase the overall compression ratio.

<p>In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.</p>

This paper presents a derivation of the Runge-Kutta or fourth method with six stages suitable for parallel implementation. Development of a parallel model based on the sparsity structure of the fourth type Runge-Kutta which is divided into three processors. The calculation of the parallel computation model and the sequential model from the accurate side shows that the sequential model is better. However, generally, the parallel method will end the analytic solution by increasing the number of iterations. In terms of execution time, parallel method has advantages over sequential method.

Universal coding was developed for compressing data that the probability distribution of a symbol is unknown. Universal coding methods encode a symbol using some bits as code based on the coding algorithm. This research introduces a new concept of universal coding by encoding two symbols using Reversible Low Contrast Mapping (RLCM) transform. The proposed method transforms a pair non negative integer to a non-negative integer and a binary number. This research also introduces a compression scheme namely Average Encoding (AE) to compress a sequence of data using the proposed coding method. This method generally yields a higher compression ratio than the Elias Delta, Elias Gamma, and Fibonacci coding when it is tested using the results of differential encoding of some testing images.

<p>In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.</p>