Different from traditional source coding techniques, distributed source coding (DSC) techniques rely on independent encoding at the encoding end but joint decoding at the decoding end to compress image sources which exhibit correlation. Channel code-based DSC techniques compress correlated image sources by fully utilizing such correlation. However, in addition to this correlation information, the current symbol of most real image sources is correlated to the preceding or subsequent symbols. Such correlation information should also play an important role for improving the compression performance of channel code-based DSC schemes. To this end, we present an efficient message passing mechanism for LDPC Code-based DSC schemes (ELCDSC) to compress correlated image sources. By utilizing this message passing mechanism, we enable LDPC code-based DSC techniques to not only make full use of the inter-source correlation to assist compression, but also integrate the intra-source correlation in each message passing iteration to improve the compression performance. It is the first that enables LDPC code-based DSC techniques to achieve the utilization of both intra- and inter-source correlations in the message passing mechanism. Experimental results reveal that ELCDSC significantly enhances the compression ratio of correlated image sources, surpassing other DSC schemes.

BACKGROUND: Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. RESULTS: In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. CONCLUSIONS: CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .

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.

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/SCCG. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

How to resist combined geometric attacks effectively while maintain a high embedding capacity is still a challenging task for the digital watermarking research. An affine correction based algorithm is proposed in this paper, which can resist combined geometric attacks and keep a higher watermark embedding capacity. The SURF algorithm and the RANSAC algorithm are used to extract, match and select feature points from the attacked image and the original image. Then, the least square algorithm is used to estimate the affine matrix of the geometric attacks according to the relationship between the matched feature points. The attacks are corrected based on the estimated affine matrix. A fine correction step is included to improve the precision of the watermark detection. To resist the cropping attacks, the watermark information is encoded with LT-coding. The encoded watermark is embedded in the DWT-DCT composite domain of the image. Experimental results show that the proposed algorithm not only has a high embedding capacity, but also is robust to many kinds of geometric attacks.

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.