A new method for blind watermarking based on quantization is proposed. The proposed scheme embeds a watermark on the lowest wavelet subband in order to be robust. Experimental results demonstrate the robustness of the algorithm against compression and other image processing attacks.

We introduce an extended EZW coder that uses flexible zerotree coding of wavelet coefficients. A flexible parent-child relationship is defined so as to exploit spatial dependencies within a subband as well as hierarchical dependencies among multi-scale subbands. The new relationship is based on a particular statistics that a large coefficient is more likely to have large coefficients in its neighborhood in terms of space and scale. In the flexible relationship, a parent coefficient in a subband relates to four child coefficients in the next finer subband in the same orientation. If each of the children is larger than a given threshold, the parent extends its parentship to the neighbors close to its conventional children. A probing bit is introduced to indicate whether a significant parent has significant children to be scanned. This enables us to avoid excessive scan of insignificant coefficients. Also, produced symbols are re-symbolized into simple variable-length binary codes to remove some redundancy according to a pre-defined rule. As a result, the wavelet coefficients can be described with a small number of binary symbols. This binary symbol stream gives a competitive performance without an additional entropy coding and thus a fast encoding/decoding is possible. Moreover, the binary symbols can be more compressed by an adaptive arithmetic coding. Our experimental results are given in both binary-coded mode and arithmetic-coded mode. Also, these results are compared with those of the EZW coder.

In this paper, we aim to investigate the potential usefulness of machine learning in image quality assessment (IQA). Most previous studies have focused on designing effective image quality metrics (IQMs), and significant advances have been made in the development of IQMs over the last decade. Here, our goal is to improve prediction outcomes of “any” given image quality metric. We call this the “IQM's Outcome Improvement” problem, in order to distinguish the proposed approach from the existing IQA approaches. We propose a method that focuses on the underlying IQM and improves its prediction results by using machine learning techniques. Extensive experiments have been conducted on three different publicly available image databases. Particularly, through both 1) in-database and 2) cross-database validations, the generality and technological feasibility (in real-world applications) of our machine-learning-based algorithm have been evaluated. Our results demonstrate that the proposed framework improves prediction outcomes of various existing commonly used IQMs (e.g., MSE, PSNR, SSIM-based IQMs, etc.) in terms of not only prediction accuracy, but also prediction monotonicity.

A zerotree image-coding scheme is introduced that effectively exploits the inter-scale self-similarities found in the octave decomposition by a wavelet transform. A zerotree is useful for efficiently coding wavelet coefficients; its efficiency was proved by Shapiro's EZW. In the EZW coder, wavelet coefficients are symbolized, then entropy-coded for further compression. In this paper, we analyze the symbols produced by the EZW coder and discuss the entropy for a symbol. We modify the procedure used for symbol-stream generation to produce lower entropy. First, we modify the fixed relation between a parent and children used in the EZW coder to raise the probability that a significant parent has significant children. The modified relation is flexibly modified again based on the observation that a significant coefficient is more likely to have significant coefficients in its neighborhood. The three relations are compared in terms of the number of symbols they produce.