A new wavelet-based method for the compression of electrocardiogram (ECG) data is presented. A discrete wavelet transform (DWT) is applied to the digitized ECG signal. The DWT coefficients are first quantized with a uniform scalar dead-zone quantizer, and then the quantized coefficients are decomposed into four symbol streams, representing a binary significance stream, the signs, the positions of the most significant bits, and the residual bits. An adaptive arithmetic coder with several different context models is employed for the entropy coding of these symbol streams. Simulation results on several records from the MIT-BIH arrhythmia database show that the proposed coding algorithm outperforms some recently developed ECG compression algorithms.

Context modeling is widely used in image coding to improve the compression performance. However, with no special treatment, the expected compression gain will be cancelled by the model cost introduced by high order context models. Context quantization is an efficient method to deal with this problem. In this paper, we analyze the general context quantization problem in detail and show that context quantization is similar to a common vector quantization problem. If a suitable distortion measure is defined, the optimal context quantizer can be designed by a Lloyd style iterative algorithm. This context quantization strategy is applied to an embedded wavelet coding scheme in which the significance map symbols and sign symbols are directly coded by arithmetic coding with context models designed by the proposed quantization algorithm. Good coding performance is achieved.

In some video compressive sensing (CS) applications, the sparsity of original signals is unknown to the sampling device. The computing power, memory space and power consumption of the sampling device are also limited, which makes it difficult to achieve adaptive rate compressive sensing (ARCS). A new blocked ARCS method for surveillance videos is proposed, which fully considers the limitations mentioned above. By observing the result of CS measurement, the statistical characteristics of the original signal are estimated. The sparsity of the original signal is reasonably estimated by using these statistical characteristics. Therefore, blocks can be divided into more classes with higher accuracy. The proposed method has the advantages of low computational complexity, small memory footprint and low power consumption, which makes it suitable for implementing in applications such as wireless video sensor networks (WVSN) and single pixel cameras (SPC). The experiment results show that the proposed method can well adapt to the change of sparsity, allocate appropriate sampling rate for each block, effectively reduce the sampling rate, and improve the quality of the reconstructed image. Meanwhile, the amount of calculation in the sampling process is much lower, and the sampling speed is obviously accelerated. The overall performance of the proposed method is better than the previous state-of-the-art method.

Ultrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details. To overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels. The results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metrics, such as peak signal to noise ratio, SNR and root mean squared error. The experimental results show that the proposed method is effective for removing the speckle and the background noise in ultrasound images. The main reason is that it performs a “detect and replace” two-step mechanism. The advantages of the proposed RBSF lie in two aspects. Firstly, each central pixel is classified as noise, speckle or noise-free texture according to the absolute difference between the target pixel and the reference median. Subsequently, the Rayleigh-maximum-likelihood filter and the bilateral filter are switched to eliminate speckle and noise, respectively, while the noise-free pixels are unaltered. Therefore, it is implemented with better accuracy and robustness than the traditional methods. Generally, these traits declare that the proposed RSBF would have significant clinical application.

Motivation: With the development and the gradually popularized application of next-generation sequencing technologies (NGS), genome sequencing has been becoming faster and cheaper, creating a massive amount of genome sequence data which still grows at an explosive rate. The time and cost of transmission, storage, processing and analysis of these genetic data have become bottlenecks that hinder the development of genetics and biomedicine. Although there are many common data compression algorithms, they are not effective for genome sequences due to their inability to consider and exploit the inherent characteristics of genome sequence data. Therefore, the development of a fast and efficient compression algorithm specific to genome data is an important and pressing issue.Results: We have developed a referential lossless genome data compression algorithm with better performance than previous algorithms. According to a carefully designed matching strategy selection mechanism, the advantages of local matching and global matching are reasonably combined together to improve the description efficiency of the matched sub-strings. The effects of the length and the position of matched sub-strings to the compression efficiency are jointly taken into consideration. The proposed algorithm can compress the FASTA data of complete human genomes, each of which is about 3 GB, in about 18 min. The compressed file sizes are ranging from a few megabytes to about forty megabytes. The averaged compression ratio is higher than that of the state-of-the-art genome compression algorithms, the time complexity is at the same order of the best-known algorithms.Availability and implementation: https://github.com/jhchen5/SCCGSupplementary information: Supplementary data are available at Bioinformatics online.