Abstract
A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of 2.0272 for DCT, 1.179 for WT and 4.301 for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of 0.1108 for DCT, 0.051 for WT and 0.175 for KLT.
Highlights
With the significant increase of multimedia technology in mobile devices and diverse applications, image compression is essential in reducing the amount of data
The lossy image compression techniques could be implemented using data transformation such as Discrete Cosine Transform (DCT) to obtain an image of type JPEG
As mentioned in the previous section, three image transformations were used, two of them are independent of the image content which are Discrete Cosine Transform (DCT) which produces JPEG and Wavelet Transform (WT)
Summary
With the significant increase of multimedia technology in mobile devices and diverse applications, image compression is essential in reducing the amount of data. Large amounts of images transfer between mobile devices through wireless communication requiring a fast and robust scheme for image compression. Biometric authentication in mobile devices represents another application which demands image compression This application uses the image modality such as faces, iris, and eyebrows to identify people via matching process with the template stored in remote database. Image compression has been implemented using curve fitting models as in the work of Khalaf et al [13], which was derived from a hyperbolic tangent function with only three coefficients In this regard, the used function had the benefits of a symmetric property to minimize the construction error and to enhance some details with texture for the reconstructed image.
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