Template-generated videos (TGVs), created by applying animation templates to static images, have become increasingly prevalent, producing massive user-generated content with highly consistent motion patterns. However, existing video compression schemes are designed to eliminate motion redundancy within individual videos, while overlooking the shared motion patterns widespread across TGVs. To address this limitation, we propose a novel compression scheme that effectively leverages inter-video motion priors to enhance the compression efficiency of TGVs. Specifically, the proposed scheme operates as a two-stage pipeline. In the first stage, high-quality motion priors are identified from a representative TGV based on spatial texture and prediction error. In the second stage, these motion priors are intelligently integrated to expand the motion representation space beyond the local candidate lists in Merge and AMVP modes, thereby enabling the codec to remove inter-video redundancy. Experimental results on the versatile video coding test model (VTM-23.0) demonstrate consistent coding gains across various compression scenarios for TGVs, achieving average BD-rate savings of$1.07 \%, 1.38 {\%}$, and 1.18% under low-delay P (LDP), low-delay B (LDB), and random access (RA) configurations, respectively.

The popularity of template-generated videos has recently experienced a significant increase on social media platforms. In general, videos from the same template share similar temporal characteristics, which are unfortunately ignored in the current compression schemes. In view of this, we aim to examine how such temporal priors from templates can be effectively utilized during the compression process for template-generated videos. First, a comprehensive statistical analysis is conducted, revealing that the coding decisions, including the merge, non-affine, and motion information, across template-generated videos are strongly correlated. Subsequently, leveraging such correlations as prior knowledge, a simple yet effective prior-driven compression scheme for template-generated videos is proposed. In particular, a mode decision pruning algorithm is devised to dynamically skip unnecessarily advanced motion vector prediction (AMVP) or affine AMVP decisions. Moreover, an improved AMVP motion estimation algorithm is applied to further accelerate reference frame selection and the motion estimation process. Experimental results on the versatile video coding (VVC) platform VTM-23.0 demonstrate that the proposed scheme achieves moderate time reductions of 14.31% and 14.99% under the Low-Delay P (LDP) and Low-Delay B (LDB) configurations, respectively, while maintaining negligible increases in Bjøntegaard Delta Rate (BD-Rate) of 0.15% and 0.18%, respectively.

This paper proposes a highly efficient sample adaptive offset (SAO) mode decision algorithm. By leveraging both the directional correlations between the SAO and intra-prediction decisions, and the SAO decisions' spatial correlations, the SAO mode candidates are effectively pruned during the rate-distortion optimization process, accelerating the SAO encoding process with negligible BD-rate loss.

Deep learning has shown its great potential in video coding. Lots of research has been done regarding deep tools used within traditional schemes or end-to-end deep codec. In this paper, we propose multiple novel deep tools focusing on intra prediction, motion compensation refinement, virtual reference frame, and post-processing and form a deep learning-assisted video compression framework. For intra prediction, a novel data clustering-driven neural network (DCDNN), which could learn deep features of the clustered data, is proposed to assist intra angular modes. For inter prediction, a neural network-based motion compensation refinement (NNMCR) algorithm is applied to enhance the motion compensation. Meanwhile, a V-RF network is employed to generate a more reliable reference frame that can characterize complex motions in natural video signals. For post-processing, a quality adaptive neural network-based in-loop filter (QANNLF) is designed to utilize Quality Parameter (QP) information in the encoder to adjust filter strength for reconstructed videos with various qualities. Our method can obtain on average 14.83% BD-rate saving for the luma component under the all intra configuration and 12.16% under the random access configuration compared with HEVC reference software 16.9.

Recommendation systems, social network analysis, medical imaging, and data mining often involve processing sparse high-dimensional data. Such high-dimensional data are naturally represented as tensors, and they cannot be efficiently processed by conventional matrix or vector computations. Sparse Tucker decomposition is an important algorithm for compressing and analyzing these sparse high-dimensional datasets. When energy efficiency and data privacy are major concerns, hardware accelerators on resource-constraint platforms become crucial for the deployment of tensor algorithms. In this work, we propose a hybrid computing framework containing CPU and FPGA to accelerate sparse Tucker factorization. This algorithm has three main modules: 1) tensor-times-matrix (TTM); 2) Kronecker products; and 3) QR decomposition with column pivoting (QRP). In addition, we accelerate the former two modules on a Xilinx FPGA and the latter one on a CPU. Our hybrid platform achieves$23.6 \times \sim 1091\times $speedup and over 93.519% ~ 99.514% energy savings compared with CPU on the synthetic and real-world datasets.

SUMMARY: Since the idea of pan-genomics emerged several tools and pipelines have been introduced for prokaryotic pan-genomics. However, not a single comprehensive pipeline has been reported which could overcome multiple challenges associated with eukaryotic pan-genomics. To aid the eukaryotic pan-genomic studies, here we present ppsPCP pipeline which is designed for eukaryotes especially for plants. It is capable of scanning presence/absence variants (PAVs) and constructing a fully annotated pan-genome. We believe with these unique features of PAV scanning and building a pan-genome together with its annotation, ppsPCP will be useful for plant pan-genomic studies and aid researchers to study genetic/phenotypic variations and genomic diversity. AVAILABILITY AND IMPLEMENTATION: The ppsPCP is freely available at github DOI: https://doi.org/10.5281/zenodo.2567390 and webpage http://cbi.hzau.edu.cn/ppsPCP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.