Jens-Rainer Ohm’s research while affiliated with RWTH Aachen University and other places

Background In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.

Background: In recent years, the advances in high-throughput sequencing technologies enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal to identify gene sequence variations to better understand phenotypic variations. We present a novel approach for the compression of gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row-and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off in an evaluation of compression and random access compared to the state-of-the-art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.

Versatile Video Coding (VVC) was finalized in July 2020 as the most recent international video coding standard. It was developed by the Joint Video Experts Team (JVET) of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) to serve an ever-growing need for improved video compression as well as to support a wider variety of today’s media content and emerging applications. This paper provides an overview of the novel technical features for new applications and the core compression technologies for achieving significant bit rate reductions in the neighborhood of 50% over its predecessor for equal video quality, the High Efficiency Video Coding (HEVC) standard, and 75% over the currently most-used format, the Advanced Video Coding (AVC) standard. It is explained how these new features in VVC provide greater versatility for applications. Highlighted applications include video with resolutions beyond standard- and high-definition, video with high dynamic range and wide color gamut, adaptive streaming with resolution changes, computer-generated and screen-captured video, ultralow-delay streaming, 360° immersive video, and multilayer coding e.g., for scalability. Furthermore, early implementations are presented to show that the new VVC standard is implementable and ready for real-world deployment.

In this Special Section of the IEEE Transactions on Circuits and Systems for Video Technology, it is our honor to introduce the Versatile Video Coding (VVC) standard, the latest of the historic partnership collaborations between the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), the International Organization for Standardization (ISO), and the International Electrotechnical Commission (IEC) in the field of video coding standardization.

In this paper, a novel approach for learning a low-dimensional optimized feature space for image retrieval with minimum intra-class variance and maximum inter-class variance is proposed. The classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low-dimensional feature space for single-labeled images. Since image retrieval involves images with multiple objects, LDA cannot be directly used for dimensionality reduction and feature space optimization. This problem is addressed by utilizing the relationship between LDA and Canonical Correlation Analysis (CCA) eigenvalues to generate an optimized feature space for both single-labeled and multi-labeled images. A CCA-based network architecture which correlates the low-dimensional feature vectors with the image label vectors is proposed. We design a novel loss function such that the correlation coefficients of CCA are maximized. Our experiments prove that we could train the neural network to reach the theoretical lower bound of loss corresponding to the negative sum of the correlation coefficients. Once the optimized feature space is generated, feature vectors are binarized with the Iterative Quantization (ITQ) approach. Finally, we propose an ensemble network to generate binary codes of desired bit length for retrieval. The measurement of mean average precision shows that the proposed approach outperforms the retrieval results of other single-labeled and multi-labeled image retrieval benchmarks at same bit numbers in a considerable number of cases.

In this paper, we propose an approach for learning binary hash codes for image retrieval. Canonical Correlation Analysis (CCA) is used to design two loss functions for training a neural network such that the correlation between the two views to CCA is maximized. The first loss, maximizes the correlation between the hash centers and learned hash codes. The second loss maximizes the correlation between the class labels and classification scores. A novel weighted mean and thresholding based hash center update scheme is proposed for adapting the hash centers in each epoch. The training loss reaches the theoretical lower bound of the proposed loss functions, showing that the correlation coefficients are maximized during training and substantiating the formation of an efficient feature space for image retrieval. The measured mean average precision shows that the proposed approach outperforms other state-of-the-art approaches in both single-labeled and multi-labeled image datasets.