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An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset. Index Terms--AREA, ECG compression, vector quantization (VQ).

Increasing the bandwidth or bit rate in real-time video applications to improve the quality of images is typically impossible or too expensive. Postprocessing appears to be the most feasible solution because it does not require any existing standards to be changed. Markedly reducing blocking effects can increase compression ratios for a particular image quality or improve the quality with respect to the specific bit rate of compression. This paper proposes a novel deblocking algorithm based on three filtering modes in terms of the activity across block boundaries. By properly considering the masking effect of the human visual system, an adaptive filtering decision is integrated into the deblocking process. According to three different deblocking modes appropriate for local regions with different characteristics, the perceptual and objective quality are improved without over smoothing the image details or insufficient reducing the strong blocking effect on the flat region. According to the simulation results, the proposed method outperforms methods of deblocking MPEG-4 with respect to peak signal-to-noise ratios and computational complexity. Index Terms--Blocking effects, human visual system (HVS), MPEG4.

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies. Index Terms--Electrocardiogram (ECG) compression, set partitioning in hierarchical trees (SPIHT), wavelet transform.

This paper proposes a circuit-sharing approach to improve efficiency for the key Digital Audio Broadcasting(DAB) techniques, i.e., MPEG1-audio decoding and orthogonal frequency division multiplexing (OFDM). Because OFDM's Fast Fourier Transform (FFT) requires heavy computational power for implementation, a single butterfly processing element (BPE) is adopted to reduce the chip area required for FFT. Furthermore, by modifying the BPE logical combinational circuit, both IMDCT (Inverse Modified Discrete Cosine Transform) and FFT functions can be obtained simultaneously from a single VLSI chip. Therefore, the proposed technique reduces hardware overhead, enhances circuit efficiency and significantly reduces the cost of DAB receivers. The proposed circuit is simulated as a VLSI prototype chip using a 0.35 [micro]m CMOS process, with a chip area of about 22.09 [mm.sup.2] and a total gate count of approximately 10839 (excluding ROM and RAM). Index Terms--Circuit sharing, COFDM, digital audio broadcasting, IMDCT, VLSI.

This brief presents a novel algorithm for speeding up the codebook design in image vector quantization that exploits the correlation among the pixels in an image block to compress the computational complexity of calculating the squared Euclidean distortion measures, and uses the similarity between the codevectors in the consecutive codebooks during the iterative clustering process to reduce the number of codevectors necessary to be checked for one codebook search. Verified test results have shown that the proposed algorithm can provide almost 98% reduction of the execution time when compared to the conventional Linde-Buzo - Gray (LBG) algorithm. Index Terms - Codebook design, integral projections, pattern clustering, vector quantization.

This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established on the integral projections, having the advantages of being good block features and having simple complexity in measuring matching errors. Most of the candidate blocks can be rejected only by calculating one or more of the three fast matching error measures. The time-consuming computations of MSE or MAE are performed on only a few candidate blocks that first pass all three fast matching criteria. Simulation results show that a reduction of over 86% in computations is achieved after integrating the three fast matching criteria into the full-search algorithm, while ensuring optimal accuracy.