Long-Wen Chang

A unified systolic array for discrete cosine and sine transforms

A linear systolic array for the discrete cosine transform, discrete sine transform, and their inverses is developed. It generates the transform kernel values recursively. Compared to the scheme with the transform kernel values prestored in memory either inside or outside each processing element, the clock period is shortened by a memory access time. In addition, the array pays no cost for prestorage. The systolic array has the advantages of pipelinability, regularity, locality, and scalability, making it quite suitable for VLSI signal processing. >

Systolic arrays for the discrete Hartley transform

Recently, R.N. Bracewell (1983) introduced the discrete Hartley transform (DHT) as an alternative to the discrete Fourier transform (DFT). Two linear systolic array models for the (DHT) are derived. One model requires O(2N-1) in the computational phase and O(N) in the preloading phase. The other model requires O(2N-1) in the computational phase and O(N) in the output phase. A square systolic array for two-dimensional DHT is also constructed by combining the individual advantages of each model. The CORDIC algorithm is proposed as an alternative to conventional multipliers. To speed up the systolic array, two-level pipelining with CORDIC is also possible. >

Designing JPEG quantization tables based on human visual system

In this paper, we propose a systematic procedure to design a quantization table based on the human visual system model for the baseline JPEG coder. By incorporating the human visual system model with a uniform quantizer, a perceptual quantization table is derived. The quantization table can be easily adapted to the specified resolution for viewing and printing. Experimental results indicate that the derived HVS-based quantization table can achieve better performance in rate-distortion sense than the JPEG default quantization table.

Hybrid image matching combining Hausdorff distance with normalized gradient matching

Image matching has been a central problem in computer vision and image processing for decades. Most of the previous approaches to image matching can be categorized into the intensity-based and edge-based comparison. Hausdorff distance has been widely used for comparing point sets or edge maps since it does not require point correspondences. In this paper, we propose a new image similarity measure combining the Hausdorff distance with a normalized gradient consistency score for image matching. The normalized gradient consistency score is designed to compare the normalized image gradient fields between two images to alleviate the illumination variation problem in image matching. By combining the edge-based and intensity-based information for image matching, we are able to achieve robust image matching under different lighting conditions. We show the superior robustness property of the proposed image matching technique through experiments on face recognition under different lighting conditions.

A High Secure Reversible Visible Watermarking Scheme

A novel reversible visible watermarking algorithm is proposed. It can fully remove the watermark from the visible watermarked image such that the original image can be restored. Pixel values of original image beneath the watermark are mapped to a small range [alpha, alpha + 127] to generate a visible watermarked image. Since the mapping is many-to-one, taking inverse mapping can only approximate the original image. To restore the original image, the difference image of subtracting the approximated image from the original image and other side information are losslessly compressed to be embedded in the visible watermarked image by a reversible data embedding algorithm. We proposed a key-based scheme for the compromise between transparency and robustness. The key is a random variable with discrete normal distribution. In addition, only users with correct key can restore the original image. In the experimental results, we show the transparent degree of watermark can be controlled by the variance of the key. Users with wrong key can not restore the original image from the visible watermarked image.

Secure reversible visible image watermarking with authentication

This paper proposes a secure reversible visible watermarking approach. The proposed pixel mapping function superposes a binary watermark image on a host image to create an intermediate visible watermarked image. Meanwhile, an almost inverse function generates the recovery data for restoring the original pixels. To prevent unauthorized users from approximating the original pixels in the watermarked region, this method adds an integer sequence in the intermediate watermarked image. The sequence is composed of integers generated by two random variables having normal distributions with zero means and distinct variances. The variances facilitate a trade-off between the watermark transparency and the noise generated by unauthorized users. The proposed method also uses Lagrange multipliers to find the optimized variances for the trade-off. Finally, this method uses reversible data embedding to embed the recovery data and hash value for reversibility and authentication, respectively. Experimental results show the watermark visibility for test images along with the watermark transparency for different variances. Using the optimized variances, the watermarked image is at the balance between the watermark transparency and the unauthorized-user-generating noise.

A bit-level systolic array for median filter

A bit-level systolic array for median filters based on the majority of the bit strings of input samples is presented. The majority function can be implemented by a 1-b odd/even transposition network, which is regular and can be designed easily. If each input sample is represented by r bits, the systolic array contains r processor elements and requires time complexity O(N+n+r-1) for a stream of N samples with window size n in a one-dimensional median filter. >

Robust face imagematching under illumination variations

Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions.

A bit level systolic array for Walsh-Hadamard transforms

Abstract The Walsh-Hadamard transforms are very important in image data compression, filtering and design of codes. Recently, a systolic array for the Hadamard transform was developed for the speed of computation. However, it requires the addition or subtraction of the input samples in word level. To increase the speed a bit level systolic array is developed. The bit level array outperforms the word level array in computation time, latency time and hardware complexity.

Predictive subband image coding with wavelet transform

Wavelet transform can decompose images into various multiresolution subbands. In these subbands the correlation exists. A novel technique for image coding by taking advantage of the correlation is addressed. It is based on predictive edge detection from the LL band of the lowest resolution level to predict the edge in the LH, HL and HH bands in the higher resolution level. If the coefficient is predicted as an edge it is preserved; otherwise, it is discarded. In the decoder, the location of the preserved coefficients can also be found as in the encoder. Therefore, no overhead is needed. Instead of complex vector quantization, which is commonly used in subband image coding for high compression ratio, simple scalar quantization is used to code the remaining coefficients and achieves very good results.