Maximizing Image Information Using Multi-Chimera Transform Applied on Face Biometric Modality

Abstract

With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.

A Systematic Multichimera Transform for Color Image Representation

“…A set of analyses were conducted to show the contribution of different parameters in the proposed transform. In the experiments, the quality of the SMTC is assessed by estimating the value of the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the compression ratio (CR) [18,24,25]. The PSNR estimates the quality of the reconstructed (decoded) images and it is inversely proportional to the mean square error (MSE).…”

“…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 transform achieved superior performance than that of methods in [22,23], but its performance also deteriorated when image blocks became larger than 4 × 4. To mitigate this drawback, a multichimera transform was developed in [25]. This transform splits the image into multiple blocks of size 16 × 16 pixels.…”

“…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.…”

“…If the image is represented as a set of functions, the detection and removal of the background could be simpler because the modification of some coefficients in the function is expected to remove the image background. Our previous work in [25] applied the multichimera transform to image analysis and reconstruction.…”