MOTIVATION: In an effort to provide a response to the ever-expanding generation of genomic data, the International Organization for Standardization (ISO) is designing a new solution for the representation, compression and management of genomic sequencing data: the Moving Picture Experts Group (MPEG)-G standard. This paper discusses the first implementation of an MPEG-G compliant entropy codec: GABAC. GABAC combines proven coding technologies, such as context-adaptive binary arithmetic coding, binarization schemes and transformations, into a straightforward solution for the compression of sequencing data. RESULTS: We demonstrate that GABAC outperforms well-established (entropy) codecs in a significant set of cases and thus can serve as an extension for existing genomic compression solutions, such as CRAM. AVAILABILITY AND IMPLEMENTATION: The GABAC library is written in C++. We also provide a command line application which exercises all features provided by the library. GABAC can be downloaded from https://github.com/mitogen/gabac. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High Dynamic Range (HDR) imaging captures a wide range of luminance existing in real-world scenes. Due to large luminance levels and higher brightness of HDR displays, artefacts can be more noticeable to the Human Visual System (HVS). In a first attempt to experimentally quantify those noticeable levels for HDR images, we pioneered in conducting an exhaustive and comprehensive Just Noticeable Difference (JND) subjective experiment of which the outcome is presented in this paper. Six distortions including JPEG, JPEG2000, noise, blur, contrast change, and quantization artefacts have been considered in the test. The distortions were applied to 10 HDR images in 100 distortion levels resulting a database of 6000 HDR test images. The subjects were asked to find the image JND location on each set of 100 images they had the freedom to explore. The effect of content features on the noticeable threshold selection is investigated per distortion type. Our results in some cases show a significant correlation between content features and JNDs. We are hoping that our results can contribute to further exploitation of a precise HVS model for HDR quality assessment and optimization of the coding and bit allocation in HDR compression.

Motivation: The past decade has seen the introduction of new technologies that significantly lowered the cost of genome sequencing. As a result, the amount of genomic data that must be stored and transmitted is increasing exponentially. To mitigate storage and transmission issues, we introduce a framework for lossless compression of quality scores. Results: This article proposes AQUa, an adaptive framework for lossless compression of quality scores. To compress these quality scores, AQUa makes use of a configurable set of coding tools, extended with a Context-Adaptive Binary Arithmetic Coding scheme. When benchmarking AQUa against generic single-pass compressors, file sizes are reduced by up to 38.49% when comparing with GNU Gzip and by up to 6.48% when comparing with 7-Zip at the Ultra Setting, while still providing support for random access. When comparing AQUa with the purpose-built, single-pass, and state-of-the-art compressor SCALCE, which does not support random access, file sizes are reduced by up to 21.14%. When comparing AQUa with the purpose-built, dual-pass, and state-of-the-art compressor QVZ, which does not support random access, file sizes are larger by 6.42-33.47%. However, for one test file, the file size is 0.38% smaller, illustrating the strength of our single-pass compression framework. This work has been spurred by the current activity on genomic information representation (MPEG-G) within the ISO/IEC SC29/WG11 technical committee. Availability and implementation: The software is available on Github: https://github.com/tparidae/AQUa. Contact: [email protected].

MOTIVATION: The past decade has seen the introduction of new technologies that lowered the cost of genomic sequencing increasingly. We can even observe that the cost of sequencing is dropping significantly faster than the cost of storage and transmission. The latter motivates a need for continuous improvements in the area of genomic data compression, not only at the level of effectiveness (compression rate), but also at the level of functionality (e.g. random access), configurability (effectiveness versus complexity, coding tool set …) and versatility (support for both sequenced reads and assembled sequences). In that regard, we can point out that current approaches mostly do not support random access, requiring full files to be transmitted, and that current approaches are restricted to either read or sequence compression. RESULTS: We propose AFRESh, an adaptive framework for no-reference compression of genomic data with random access functionality, targeting the effective representation of the raw genomic symbol streams of both reads and assembled sequences. AFRESh makes use of a configurable set of prediction and encoding tools, extended by a Context-Adaptive Binary Arithmetic Coding scheme (CABAC), to compress raw genetic codes. To the best of our knowledge, our paper is the first to describe an effective implementation CABAC outside of its' original application. By applying CABAC, the compression effectiveness improves by up to 19% for assembled sequences and up to 62% for reads. By applying AFRESh to the genomic symbols of the MPEG genomic compression test set for reads, a compression gain is achieved of up to 51% compared to SCALCE, 42% compared to LFQC and 44% compared to ORCOM. When comparing to generic compression approaches, a compression gain is achieved of up to 41% compared to GNU Gzip and 22% compared to 7-Zip at the Ultra setting. Additionaly, when compressing assembled sequences of the Human Genome, a compression gain is achieved up to 34% compared to GNU Gzip and 16% compared to 7-Zip at the Ultra setting. AVAILABILITY AND IMPLEMENTATION: A Windows executable version can be downloaded at https://github.com/tparidae/AFresh . CONTACT: [email protected].

In previous work, the authors developed a modular no-reference framework that compresses FASTA files by applying a predict-and-residue method, as used in video coding. We extended this framework with support for Context-Adaptive Binary Arithmetic Coding (CABAC), while at the same time preserving random access functionality and offering support for the full IUB/IUPAC nucleic acid codes alphabet.

High-dynamic-range (HDR) technology allows capturing of video content at a wider range of luminance than low-dynamic-range (LDR) video. The resulting video more closely resembles the scene as perceived by the human eye. However, displays currently support only a limited range of HDR. Therefore, both an HDR version and LDR version of a video should be encoded during content acquisition. This means that the cost of encoder hardware in cameras would double. As a solution, this paper proposes a multistream video encoder that allows generating an HDR and LDR version of the same HDR video footage at roughly the same computational complexity as a single encoder, effectively allowing encoding of two dynamic-range versions of the video with a negligible increase in cost. For the LDR version, this multistream encoder results in a bit rate overhead of only 11.6% for the same quality as a two-encoder solution.

With the introduction of 4K UHD video and display resolution, questions arise on the perceptual differences between 4K UHD and upsampled HD video content. In this paper, a striped pair comparison has been performed on a diverse set of 4K UHD video sources. The goal was to subjectively assess the perceived sharpness of 4K UHD and downscaled/upscaled HD video. A striped pair comparison has been applied in order to make the test as straightforward as possible for a non-expert participant population. Under these conditions and over this set of sequences, on average, on 54.8% of the sequences (17 out of 31), 4K UHD resolution content could be identified as being sharper compared to its HD down and upsampled alternative. The probabilities in which 4K UHD could be differentiated from downscaled/upscaled HD range from 83.3% for the easiest to assess sequence down to 39.7% for the most difficult sequence. Although significance tests demonstrate there is a positive sharpness difference from camera quality 4K UHD content compared to the HD downscaled/upscaled variations, it is very content dependent and all circumstances have been chosen in favor of the 4K UHD representation. The results of this test can contribute to the research process of developing metrics indicating visibility of high resolution features within specific content.

Multimedia content adaptation is gaining importance because of the growing amount of multimedia content on the one hand and the growing diversity in usage environments on the other hand. Furthermore, to deal with the growing amount of coding formats for multimedia content, format-independent adaptation systems are desired. These systems support the exploitation of scalability to meet the usage environment, as well as semantic adaptations to meet the user preferences. In this demonstration, we present NinSuna, a fully integrated multimedia content adaptation platform based on semantic web technologies. It aims at being deployable in streaming environments and relies on format-independent semantic-aware adaptation engines.