Chimera: A New Efficient Transform for High Quality Lossy Image Compression

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.

Fast Codebook Generation Using Pattern Based Masking Algorithm for Image Compression

“…A pattern shows regularity in an image which can be identified as a combination of progression in direction of tonal densities. A block of an image can take different forms of patterns which may contains a flat, a slope or an edge of an image [26]. Histogram values are used to predict the correlation between the neighboring pixels by finding the most frequently occurred blocks.…”

A Systematic Multichimera Transform for Color Image Representation

“…However, converting the image into a mathematical representation requires a huge amount of work and multistep analysis. Some efforts were conducted in [22][23][24][25] to convert the image into a set of mathematical representations, which demonstrated the initial steps to achieve this goal. In [22], each image block was expressed as three coefficients using a new curve-fitting technique with a hyperbolic tangent function.…”

“…This method is also not flexible enough to deal with different mathematical functions. Further improvement was accomplished in [24], in which a chimera transform was introduced. The image was split into blocks of 4 × 4 pixels, and three coefficients were then estimated from each one.…”

“…A few coefficients are estimated from each block using a precomputed codebook of size 2096 × 16 × 16. Thus, the image can be represented using a few coefficients, and a larger compression ratio can be obtained than those obtained in [22][23][24]. The disadvantage of this method is in the way in which the codebook is generated, where statistical mathematical representations and a set of images are required to construct the codebook, which complicates the whole work.…”

“…The quality of the reconstructed image is improved by recursively implementing the matching process to preserve more image details. This scheme is called systematic multichimera transform (SMCT) and represents an extension to the work in [22][23][24][25]. Efforts are ongoing to accomplish more advanced steps towards converting an image into full mathematical representations.…”