Pao-Chi Chang

On verifying the first-order Markovian assumption for a Rayleigh fading channel model

The use of received signal-to-noise ratio (SNR) as the side information in communication systems has been widely accepted especially when the channel quality is time varying. On many occasions, this side information is treated as the received SNR of the current channel symbol or that of previous symbols. In particular, the first-order Markov channel provides a mathematically tractable model for time-varying channels and uses only the received SNR of the symbol immediately preceding the current one. With the first-order Markovian assumption, given the information of the symbol immediately preceding the current one, any other previous symbol should be independent of the current one. Although the experimental measurements confirm the usefulness of the first-order Markovian assumption, one may argue that second or higher-order Markov processes should provide a more accurate model. We answer this question by showing that given the information corresponding to the previous symbol, the amount of uncertainty remaining in the current symbol should be negligible.

Hybrid LMS-MMSE inverse halftoning technique

The objective of this work is to reconstruct high quality gray-level images from bilevel halftone images. We develop optimal inverse halftoning methods for several commonly used halftone techniques, which include dispersed-dot ordered dither, clustered-dot ordered dither, and error diffusion. At first, the least-mean-square (LMS) adaptive filtering algorithm is applied in the training of inverse halftone filters. The resultant optimal mask shapes are significantly different for various halftone techniques, and these mask shapes are also quite different from the square shape that was frequently used in the literature. In the next step, we further reduce the computational complexity by using lookup tables designed by the minimum mean square error (MMSE) method. The optimal masks obtained from the LMS method are used as the default filter masks. Finally, we propose the hybrid LMS-MMSE inverse halftone algorithm. It normally uses the MMSE table lookup method for its fast speed. When an empty cell is referred, the LMS method is used to reconstruct the gray-level value. Consequently, the hybrid method has the advantages of both excellent reconstructed quality and fast speed. In the experiments, the error diffusion yields the best reconstruction quality among all three halftone techniques.

Media hash-dependent image watermarking resilient against both geometric attacks and estimation attacks based on false positive-oriented detection

The major disadvantage of existing watermarking methods is their limited resistance to extensive geometric attacks. In addition, we have found that the weakness of multiple watermark embedding methods that were initially designed to resist geometric attacks is their inability to withstand the watermark-estimation attacks (WEAs), leading to reduce resistance to geometric attacks. In view of these facts, this paper proposes a robust image watermarking scheme that can withstand geometric distortions and WEAs simultaneously. Our scheme is mainly composed of three components: 1) robust mesh generation and mesh-based watermarking to resist geometric distortions; 2) construction of media hash-based content-dependent watermark to resist WEAs; and 3) a mechanism of false positive-oriented watermark detection, which can be used to determine the existence of a watermark so as to achieve a tradeoff between correct detection and false detection. Furthermore, extensive experimental results obtained using the standard benchmark (i.e., Stirmark) and WEAs, and comparisons with relevant watermarking methods confirm the excellent performance of our method in improving robustness. To our knowledge, such a thorough evaluation has not been reported in the literature before

Robust mesh-based hashing for copy detection and tracing of images

Due to the desired non-invasive property, non-data hiding (called media hashing here) is considered to be an alternative to achieve many applications previously accomplished with watermarking. Recently, media hashing techniques for content identification have been gradually emerging. However, none of them are really resistant against geometrical attacks. In this paper, our aim is to propose a geometry-invariant image hashing scheme, which can be employed for content copy detection and tracing. Our system is mainly composed of three components: (i) robust mesh extraction; (iii) mesh-based robust hash extraction; and (iii) hash matching for similarity measurement. Exhaustive experimental results obtained from benchmark attacks have confirmed the performance of the proposed method

Error-propagation prevention technique for real-time video transmission over ATM networks

Video sequences compressed by the current video-compression standards-such as MPEG 1/2 and H.261/H.263, which include motion compensation and variable-length coding-are very sensitive to channel disturbances. There exist many error-concealment techniques that can improve the video quality substantially. However, they generally do not prevent or terminate the error propagation. The forced intraupdate technique was proposed for H.245. In this paper, we present an efficient temporal error-propagation prevention method that implements the forced intraupdate concept implicitly. Once an error is detected at the decoder, the starting address of the erroneous macroblocks is sent back to the encoder. The encoder marks the possible damaged area, and the encoding process continues as normal except that the motion estimation will probably not refer to the erroneous part. Thus, the impact of the cell loss is limited to the erroneous slice only, and the damage from the error propagation is greatly reduced. Simulation results of MPEG-2 coding over asynchronous transfer mode (ATM) networks show that the error concealment with feedback can effectively isolate the error and reduce the damage to give satisfactory performance even when the cell-loss rate is higher than 1%. With the delay analysis of ATM networks, we also show that in most cases, the encoder has adequate time to get the feedback information before processing the next I- or P-frames.

Fourier Transform Vector Quantization for Speech Coding

Design algorithms and simulation results are presented for vector quantizers for Fourier transformed data. Transforming the data prior to quantization has two potential advantages. First, each sample in the transform domain depends on many samples in the original domain. Thus, even scalar quantization in the transform domain is a form of vector quantization or block source coding in the original waveform domain and the basic coding theorems of information theory show that such block codes can provide better performance than scalar codes, even for memoryless sources. Second, vector quantization of Fourier transformed speech waveforms provides distinctly better subjective quality than ordinary vector quantization of the waveform using codes of comparable complexity. While the system is, of course, more complicated due to the need to take Fourier transforms, its envisioned application is as a coder for the output of FFT chips currently available or under development. The proposed implementation of a Fourier transform vector quantizer (FTVQ) uses a product code structure, providing different codes for different coefficient vectors corresponding to different frequency bands. This is a form of subband coding and yields a simple means of optimizing bit allocations among the subcodes. Two coding structures with corresponding distortion measures are considered: those that quantize vectors of pairs of real and imaginary coefficients and those that quantize separate vectors of magnitude and phase coefficients. Both structures yield good performance for the given complexity in comparison to waveform vector quantizers. For speech coding, a magnitude-phase FTVQ yields better subjective quality than a real-imaginary FTVQ when the rate allocation is properly chosen.

Precise and fast error tracking for error-resilient transmission of H.263 video

A precise error tracking scheme for robust transmission of real-time H.263 video is presented. By utilizing a feedback channel, the decoder reports the addresses of corrupted blocks induced by transmission errors back to the encoder. With these negative acknowledgments, the encoder can precisely calculate and track the propagated errors by examining the backward motion dependency for each pixel in the current encoding frame. With this precise tracking, the error-propagation effects can be terminated completely by INTRA refreshing the affected macroblocks. In addition, by utilizing the four-corner tracking approximation and the linear motion model, a fast algorithm is also developed to further reduce the computation and memory requirements. The simulations show that both schemes yield significant video quality improvements in error-prone environments. The advantages of the low memory requirement and the low computation complexity are particularly suitable for real time implementation.

A scalable video compression technique based on wavelet transform and MPEG coding

We present a scalable coding scheme based on the discrete wavelet transform (DWT) and MPEG coding for video applications. It utilizes the hierarchical pyramid structure that provides multiple resolutions. The DWT decomposes the image into several bands. In each band, a fixed-size motion compensated MPEG coder with custom-designed quantization tables and scanning direction is employed. This scheme has the advantages of reusing the widely available MPEG hardware and software as well as relieving the limitation of the image size imposed by the MPEG hardware technology. The simulation results show that the DWT-MPEG coding method improves the image quality over ordinary MPEG coding by 0.3/spl sim/1.5 dB.

Neural net classification and LMS reconstruction to halftone images

The objective of this work is to reconstruct high quality gray-level images from halftone images, or the inverse halftoning process. We develop high performance halftone reconstruction methods for several commonly used halftone techniques. For better reconstruction quality, image classification based on halftone techniques is placed before the reconstruction process so that the halftone reconstruction process can be fine tuned for each halftone technique. The classification is based on enhanced 1D correlation of halftone images and processed with a three- layer back propagation neural network. This classification method reached 100 percent accuracy with a limited set of images processed by dispersed-dot ordered dithering, clustered-dot ordered dithering, constrained average, and error diffusion methods in our experiments. For image reconstruction, we apply the least-mean-square adaptive filtering algorithm which intends to discover the optimal filter weights and the mask shapes. As a result, it yields very good reconstruction image quality. The error diffusion yields the best reconstructed quality among the halftone methods. In addition, the LMS method generates optimal image masks which are significantly different for each halftone method. These optimal masks can also be applied to more sophisticated reconstruction methods as the default filter masks.

Low power architecture design and hardware implementations of deblocking filter in H.264/AVC

An adaptive in-loop deblocking filter (DF) is standardized in H.264/AVC to reduce blocking artifacts and improve compression efficiency. This paper proposes a low power DF architecture with hybrid and intelligent edge skip filtering order. We further adopt a four-stage pipeline to boost the speed of DF process and the proposed Horizontal Edge Skip Processing Architecture (HESPA) offers an edge skip aware mechanism for filtering the horizontal edges that not only reduces power consumption but also reduces the filtering processes down to 100 clock cycles per macroblock (MB). In addition, the architecture utilizes the buffers efficiently to store the temporary data without affecting the standarddefined data dependency by a reasonable strategy of edge filtering order to enhance the reusability of the intermediate data. The system throughput can then be improved and the power consumption can also be reduced. Simulation results show that more than 34% of logic power measured in FPGA can be saved when the proposed HESPA is enabled. Furthermore, the proposed architecture is implemented on a 0.18μm standard cell library, which consumes 19.8K gates at a clock frequency of 200 MHz, which compares competitively with other state-of-the-art works in terms of hardware cost.