An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques

Abstract

Reduction in file size leads to reduction in the number of bits required to store it. When data is compressed, it must be decompressed into its original form bit for bit. Compound images are defined as images that contain a combination of text, natural (photo) images and graphic images. Here, compression is the process of reducing the amount of data required to represent information. Image compression is done on the basis of various loss and lossless compression algorithms. This research work deals with the preprocessing and transformations used to compress a compound image to produce a high compression ratio (CR), less compression time and so on. In the compression process the images are considered for preprocessing and discrete wavelet transform with adaptive particle swarm optimization process. The purpose of this optimization technique is to optimize the wavelet coefficient in Harr wavelet for improving the CR value. In the image compression process, run length coding is used to compress the compound images. Based on this technique, it produces minimum CR and less computation time of compound images.

Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

“…The performance of the proposed secured compression model is assessed against the other existing models like Optimal Discrete Wavelet Transform and Run Length Encoding Technique (ODWT-RLE) [40], Bit plane Run Length Coding (BRLC) [41], Multilevel Block Truncation Coding (MBTC) [41], Differential Predictive Coding (DPC) [41] that have experimented over the similar data and the mean of those obtained values are presented in Table 4 and the corresponding graphs are shown in Figure 10. The assessed values like PSNR, MSE, and RMSE make the proposed model outperforms various compression techniques.…”

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

“…DWT (Discrete Wavelet Transform) was first utilized for fast nearest-neighbor search in medical image databases by Korn et al [41], then Chan and Fu [5] applied it for the compression of time series. Besides, Jain et al [12] and Rajan and Fred [13] used 2D-DWT to compress image, and Xu et al [14] presented a DWT-based fast and high-efficient intraframe compression algorithm. In contrast to DFT, DWT is a local transformation.…”

“…The first addend X T X is the intermediate result of polynomial fitting of previous n data points, and the second addend is the difference caused by new data point (t n+1 , y n+1 ). Similarly, X T y can be expressed as (13), in which X T y is the intermediate result of last polynomial fitting, and the second addend is the change caused by the arrival of the new data point.…”

A spatial image compression algorithm based on run length encoding

“…In order to judge the efficiency of a compression scheme, a performance measurement is required [16]. The most commonly used performance measurements are: the compression ratio, the execution time of the compression and decompression processes, and the signal ❒ ISSN: 2302-9285 to noise ratio [17]- [19]. The compression ratio (CR), which is calculated using (1), is defined as the ratio between the size of the original image (N 1 ) and the size of the compressed image (N 2 ).…”