Image Compression and Watermarking scheme using Scalar Quantization

TL;DR

This paper introduces a novel image compression and watermarking scheme using Contourlet Transform, achieving better quality and robustness compared to JPEG and wavelet methods, especially for images with contours.

Contribution

The paper presents a combined compression and watermarking approach based on Contourlet Transform with energy-based quantization and double filter bank structure, improving over existing methods.

Findings

Superiority over JPEG in reducing blocking artifacts.

Better performance than wavelet transform for contour-rich images.

Watermark extraction with minimal error and high visual quality.

Abstract

This paper presents a new compression technique and image watermarking algorithm based on Contourlet Transform (CT). For image compression, an energy based quantization is used. Scalar quantization is explored for image watermarking. Double filter bank structure is used in CT. The Laplacian Pyramid (LP) is used to capture the point discontinuities, and then followed by a Directional Filter Bank (DFB) to link point discontinuities. The coefficients of down sampled low pass version of LP decomposed image are re-ordered in a pre-determined manner and prediction algorithm is used to reduce entropy (bits/pixel). In addition, the coefficients of CT are quantized based on the energy in the particular band. The superiority of proposed algorithm to JPEG is observed in terms of reduced blocking artifacts. The results are also compared with wavelet transform (WT). Superiority of CT to WT is observed when the image contains more contours. The watermark image is embedded in the low pass image of contourlet decomposition. The watermark can be extracted with minimum error. In terms of PSNR, the visual quality of the watermarked image is exceptional. The proposed algorithm is robust to many image attacks and suitable for copyright protection applications.