Wen-Jan Chen

High payload steganography mechanism using hybrid edge detector

Steganography is the art and science of hiding data into information. The secret message is hidden in such a way that no one can apart from the sender or the intended recipient. The least significant bit (LSB) substitution mechanism is the most common steganographic technique for embedding a secret message in an image with high capacity, while the human visual system (HVS) would be unable to notice the hidden message in the cover image. In this paper, besides employing the LSB substitution technique as a fundamental stage, we take advantage of edge detection technique. The experimental results show that the proposed scheme not only achieves high embedding capacity but also enhances the quality of the stego image from the HVS by an edge detection technique. Moreover, based on that the secret message is replaced with different LSBs, our scheme can effectively resist the image steganalysis.

VQ indexes compression and information hiding using hybrid lossless index coding

For the compression of memoryless vector quantization (VQ), most of the lossless index coding algorithms are not suitable for various test images. As a result, we present a hybrid dynamic tree-coding scheme (DTCS) and modified search order coding scheme (MSOC) to re-encode the output index map efficiently without causing any extra coding distortion. The main idea behind this scheme is that the adjacent left and upper around the current processed block usually provide more useful information than its adjacent left-upper and right-upper block, thus we employ two different coding methods according to their corresponding left or upper spatial relations. In addition, we applied the HLIC method to the information hiding. The proposed method does not modify the contents of the secret data and the compressed image. Experimental results show that the newly proposed algorithm achieves significant reduction of bit rate compared to the other lossless index coding schemes for various test images and different codebook sizes. The proposed information hiding scheme can hide a huge amount of information in the index map of an image and allows complete reconstruction of the indexes of the image.

Color demosaicking using deinterlacing and median-based filtering techniques

Color demosaicking is critical to the image quality of single-sensor-based imaging devices. Caused by the sampling pattern of color filter array (CFA), the demosaicked images typically suffer from visual color artifacts in regions of high frequency and sharp edge structures, degrading the quality of camera output. We present a new high-quality demosaicking algorithm by taking advantage of deinterlacing and median-based filtering techniques. We treat the sampled green data of Bayer CFA as a form of diagonal interlaced green planes and make use of some key concepts about spatial deinterlacing to help the edge estimation in terms of both various directions and accuracy. In addition, a specific edge feature, sharp line edge of width 1 pixel, can also be handed well by the proposed method. The median-based filtering techniques are developed for suppressing most visual demosaicking artifacts, such as zipper effect, false color artifact, and interpolation artifact. Experimental results show that our algorithm is effective in suppressing visual artifacts, preserving the edges of image with sharpness and satisfying visual inspection, while keeping computational efficiency.

Effective soft-decision demosaicking using directional filtering and embedded artifact refinement

Demosaicking is an interpolation process that transforms a color filter array (CFA) image into a full-color image in a single-sensor imaging pipeline. In all demosaicking techniques, the interpolation of the green components plays a central role in dictating the visual quality of reconstructed images because green light is of maximum sensitivity in the human visual system. Guided by this point, we propose a new soft-decision demosaicking algorithm using directional filtering and embedded artifact refinement. The novelty of this approach is twofold. First, we lift the constraint of the Bayer CFA that results in the absence of diagonal neighboring green color values for directionally recovering diagonal edges. The developed directional interpolation method is fairly robust in dealing with the four edge features, namely, vertical, horizontal, 45-deg diagonal, and 135-deg diagonal. In addition, the proposed embedded refinement scheme provides an efficient way for soft-decision-based algorithms to achieve improved results with fewer computations. We have compared this new approach to six state-of-the-art methods, and it can outstandingly preserve more edge details and handle fine textures well, without requiring a high computational cost.

Extraction of Line Feature in Binary Images

Thinning and line extraction of binary images not only reduces data storage amount, automatically creates the adjacency and relativity between line and points but also provides applications for automatic inspection systems, pattern recognition systems and vectorization. Based on the features of construction drawings, new thinning and line extraction algorithms were proposed in this study. The experimental results showed that the proposed method has a higher reliability and produces better quality than the various existing methods.

A sharp edge-preserving joint color demosaicking and zooming algorithm using integrated gradients and an iterative back-projection technique

Following the advances in single-sensor imaging techniques, interest in producing a zoomed full-color image from a Bayer mosaic data has been increased. Almost all of the recent approaches identified, with respect to the demosaicking step in the imaging pipeline, have chiefly focused on misguidance problems. However, in regions consisting of sharp edges or fine textures, these approaches are prone to large blurring effects. This paper proposes a new joint solution to overcome the above problems associated with demosaicking and zooming operations. On the basis of an enhanced soft-decision framework, we estimate the edge features by computing the integrated gradients. This allows the extraction of gradient information from both color intensity and color difference domains, simultaneously. Then, the edge guidance is incorporated in the interpolation of various stages to preserve edge consistency and improve computational efficiency. In addition, an edge-adaptive, iterative, back-projection technique is developed to compensate for image blurring as well as to further suppress color artifacts. Experimental results indicate that the new algorithm produces outstanding objective performances and sharp, visually pleasing color outputs, when compared to numerous other single-sensor image zooming solutions.

Bit rate reduction techniques for absolute moment block truncation coding

Abstract Block Truncation Coding uses a two‐level moment preserving quantizer that adapts to local properties of the images. It has the features of low computation load and low memory requirement while its bit rate is only 2.0 bits per pixel. A more efficient algorithm, the absolute moment BTC (AMBTC) has been extensively used in the field of signal compression because of its simple computation and better MSE performance. We propose postprocessing methods to further reduce the entropy of two output data of AMBTC, including the bit map and two quantization data (a, b). A block of a 2×4 bit map is packaged into a byte‐oriented symbol. The entropy can be reduced from 0.965 bpp to 0.917 bpp on average for our test images. The two subimages of quantization data (a, b) are postprocessed by the Peano Scan. This postprocess can further reduce differential entropy about 0.4 bit for a 4×4 block. By applying arithmetic coding, the total bit reduction is about 0.3∼0.4 bpp. The bit rate can reach 1.6∼1.7 bpp with the ...