The study of three-dimensional folding of chromosomes is important to understand genomics processes. This is done through techniques, such as Hi-C, that analyze the spatial organization of chromosomes in a cell. The data coming from the study is a 2-dimensional quantitative maps with genomic coordinate systems. We present a novel approach called Contact Matrix Compressor(CMC) for the efficient compression of Hi-C data. By exploiting the properties of the data, such as diagonally dominant and symmetrical, CMC achieves a much higher compression. CMC outperforms the existing method Cooler, and also the generic compression methods LZMA as well as BZip2.

High-throughput sequencing of RNA molecules has enabled the quantitative analysis of gene expression at the expense of storage space and processing power. To alleviate these problems, lossy compression methods of the quality scores associated to RNA sequencing data have recently been proposed, and the evaluation of their impact on downstream analyses is gaining attention. In this context, this work presents a first assessment of the impact of lossily compressed quality scores in RNA sequencing data on the performance of some of the most recent tools used for differential gene expression.

Modern video coding standards like High Efficiency Video Coding (HEVC) provide superior coding efficiency. However, this does not state true for complex and hard to predict textures which require high bit rates to achieve a high quality. To overcome this limitation of HEVC, texture synthesis frameworks were proposed in previous works. However, these frameworks only result in good reconstruction quality if the decomposition into synthesizable and non-synthesizable regions is either known or trivial. The frameworks fail for more challenging content, e.g. for content with fine non-synthesizable details within synthesizable regions. To enable texture synthesis-based video coding with high quality for this content, we propose sophisticated detail-aware decomposition techniques in this paper. These techniques are based on an initial coarse segmentation step followed by a refinement step that detects even small differences in the previously segmented region. With this new approach, we are able to achieve average luma BD-rate gains of 13.77% over HEVC and 3.03% over the closest related work from the literature. Furthermore, the considerably improved visual quality in addition to the bit rate savings is confirmed by comprehensive subjective tests.

High-throughput sequencing of RNA molecules has enabled the quantitative analysis of the expression of genes at the expense of storage space and processing power. To help alleviate these problems, lossy compression methods of the quality scores associated to RNA sequence data have recently been proposed, and the evaluation of their impact on downstream analysis is gaining attention. This work presents a first assessment of the impact of lossly compressed quality scores in RNA sequence data on the performance of some of the most recent tools used for differential gene expression.

Due to novel high-throughput next-generation sequencing technologies, the sequencing of huge amounts of genetic information has become affordable. On account of this flood of data, IT costs have become a major obstacle compared to sequencing costs. High-performance compression of genomic data is required to reduce the storage size and transmission costs. The high coverage inherent in next-generation sequencing technologies produces highly redundant data. This paper describes a compression algorithm for aligned sequence reads. The proposed algorithm combines alignment information to implicitly assemble local parts of the donor genome in order to compress the sequence reads. In contrast to other algorithms, the proposed compressor does not need a reference to encode sequence reads. Compression is performed on-the-fly using solely a sliding window (i.e. a permanently updated short-time memory) as context for the prediction of sequence reads. The algorithm yields compression results on par or better than the state-of-the-art, compressing the data down to 1.9% of the original size at speeds of up to 60 MB/s and with a minute memory consumption of only several kilobytes,fitting in today's level 1 CPU caches.

Summary form only given. Upon the completion of the single-layer H.265/HEVC, scalable extensions of the H.265/HEVC standard, called Scalable High Efficiency Video Coding (SHVC), are currently under development. Compared to the simulcast solution that simply compresses each layer separately, SHVC offers higher coding efficiency by means of inter-layer prediction which is implemented by inserting inter-layer reference (ILR) pictures generated from reconstructed base layer (BL) pictures into the enhancement layer (EL) decoded picture buffer (DPB) for motion-compensated prediction of the collocated pictures in the EL. If the EL has a higher resolution than that of the BL, the reconstructed BL pictures need to be up-sampled to form the ILR pictures. Given that the ILR picture is generated based on the reconstructed BL picture, its suitability for an efficient inter-layer prediction may be limited due to the following reasons. Firstly, quantization is usually applied when coding the BL pictures. Quantization causes the BL reconstructed texture to contain undesired coding artifacts, such as blocking artifacts, ringing artifacts, and color artifacts. Secondly, in case of spatial scalability, a down-sampling process is used to create the BL pictures. To reduce aliasing, the high frequency information in the video signal is typically removed by the down-sampling process. As a result, the texture information in the ILR picture lacks certain high frequency information. In contrast to the ILR picture, the EL temporal reference pictures contain plentiful high frequency information, which could be extracted to enhance the quality of the ILR picture. To further improve the efficiency of inter-layer prediction, a low pass filter may be applied to the ILR picture to alleviate the quantization noise introduced by the BL coding process. In this paper, an ILR enhancement method is proposed to improve the quality of the ILR picture by combining the high frequency information extracted from the EL temporal reference pictures together with the low frequency information extracted from the ILR picture. Experimental results show that the proposed method can significantly increase the ILR efficiency for EL coding, under the Common Test Condition of SHVC, which defines a number of temporal prediction structures called Random Access (RA), Low-delay B (LD-B) and Low-delay P (LD-P), on average the proposed method provides {Y, U, V} BD-rate (BL+EL) gains of {2.0%, 7.1%, 8.2%}, {2.2%, 6.7%, 7.6%} and {4.0%, 7.4%, 8.4%} for RA, LD-B, and LD-P, respectively, in comparison to the performance of the SHVC reference software SHM-2.0.