Two new coding schemes for bit-rate reduction of digital images are proposed. Both coding schemes are based on a new model which makes use of the conditional statistical properties of the image signals. These statisti...
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Two new coding schemes for bit-rate reduction of digital images are proposed. Both coding schemes are based on a new model which makes use of the conditional statistical properties of the image signals. These statistics are used to construct codebooks for the encoding and decoding of the digital images. In the first scheme, the code words are assigned in such a way as to provide a signal with long bit runs. Such a signal is then efficiently run-length coded using CCITT codes. In the second scheme, each picture element (PEL) value is coded by variable-length code words according to the values of previously transmitted PELs. The performance of both schemes in terms of entropy and bit-rate are compared with an optimum predictive coder. The simulation results indicate that the proposed schemes have a significant advantage over standard predictive encoders. A method to reduce the storage requirement for the encoder and decoder codebooks is also proposed.
A novel 2-D compression scheme of ECG signals is proposed, which employs 1-D discrete wavelet transform, the region of interest mask, and the conditional entropy coding based on context models. Experimental results on...
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A novel 2-D compression scheme of ECG signals is proposed, which employs 1-D discrete wavelet transform, the region of interest mask, and the conditional entropy coding based on context models. Experimental results on records selected from the Massachusetts Institute of Technology Beth Israel Hospital arrhythmia database show that the proposed method outperforms some existing compression schemes.
Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden for the long-term recording system and telemedicine applications. In this paper, an improved wavelet-based compression m...
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Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden for the long-term recording system and telemedicine applications. In this paper, an improved wavelet-based compression method is proposed. A discrete wavelet transform (DWT) is firstly applied to the mean removed ECG signal. DWT coefficients in a hierarchical tree order are taken as the component of a vector named tree vector (TV). Then, the TV is quantized with a vector-scalar quantizer (VSQ), which is composed of a dynamic learning vector quantizer and a uniform scalar dead-zone quantizer. The context modeling arithmetic coding is finally employed to encode those quantized coefficients from the VSQ. All tested records are selected from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. Statistical results show that the compression performance of the proposed method outperforms several published compression algorithms. (C) 2012 Elsevier Ltd. All rights reserved.
We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least sig...
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We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditionalentropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.
Recently, deep learning has inspired a shift from traditional coding to Deep Video Compression (DVC). Early DVC systems used conventional residual coding, but newer methods with superior conditional entropy coding hav...
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ISBN:
(纸本)9798350386851;9798350386844
Recently, deep learning has inspired a shift from traditional coding to Deep Video Compression (DVC). Early DVC systems used conventional residual coding, but newer methods with superior conditional entropy coding have surpassed them. Despite advancements, DVC requires multi-bitrate encoding, traditionally managed by training multiple models or different QPs. Our proposed framework achieves multi-rate encoding with a single model, significantly reducing BD-Rate by 32.406% and 66.302% for PSNR and MS-SSIM, respectively, compared to the x265 system.
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