Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

Sex Ratio and Sexual Size Dimorphism in a Toad-headed Lizard, Phrynocephalus guinanensis

Phrynocephalus guinanensis has sexual dimorphism in abdominal coloration, but its ontogenetic development of sexual size dimorphism (SSD) is unknown. Using mark-recapture data during four days each year from August from 2014 to 2016, we investigated the development of sex ratios, SSD, sex-specific survivorship and growth rates in a population of P. guinanensis. Our results indicated that the sex ratio of males to females was 1:2.8. Males had a lower survival rate (6%) than females (14%) across the age range from hatchling to adult, which supported the discovered female-biased sex ratio potentially associated with the low survival rate of males between hatchlings and juveniles. Male-biased SSD in tail length and head width existed in adults rather than in hatchling or juvenile lizards. The growth rates in body dimensions were undistinguishable between the sexes during the age from hatchling to juvenile, but the growth rate in head length from juvenile to adult was significantly larger in males than females. Average growth rate of all morphological measurements from hatchling to juvenile were larger compared with corresponding measurements from juvenile to adult, but only being significant in tail length, head width, abdomen length in females and snout-vent length in males. We provided a case study to strengthen our understanding of the important life history traits on how a viviparous lizard population can survive and develop their morphology in cold climates

Elevation as a selective force on mitochondrial respiratory chain complexes of the Phrynocephalus lizards in the Tibetan plateau

Animals living in extremely high elevations have to adapt to low temperatures and low oxygen availability (hypoxia), but the underlying genetic mechanisms associated with these adaptations are still unclear. The mitochondrial respiratory chain can provide >95% of the ATP in animal cells, and its efficiency is influenced by temperature and oxygen availability. Therefore, the respiratory chain complexes (RCCs) could be important molecular targets for positive selection associated with respiratory adaptation in high-altitude environments. Here, we investigated positive selection in 5 RCCs and their assembly factors by analyzing sequences of 106 genes obtained through RNA-seq of all 15 Chinese Phrynocephalus lizard species, which are distributed from lowlands to the Tibetan plateau (average elevation >4,500 m). Our results indicate that evidence of positive selection on RCC genes is not significantly different from assembly factors, and we found no difference in selective pressures among the 5 complexes. We specifically looked for positive selection in lineages where changes in habitat elevation happened. The group of lineages evolving from low to high altitude show stronger signals of positive selection than lineages evolving from high to low elevations. Lineages evolving from low to high elevation also have more shared codons under positive selection, though the changes are not equivalent at the amino acid level. This study advances our understanding of the genetic basis of animal respiratory metabolism evolution in extreme high environments and provides candidate genes for further confirmation with functional analyses

Learned Point Cloud Geometry Compression

This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE). In our approach, PCG is first voxelized, scaled and partitioned into non-overlapped 3D cubes, which is then fed into stacked 3D convolutions for compact latent feature and hyperprior generation. Hyperpriors are used to improve the conditional probability modeling of latent features. A weighted binary cross-entropy (WBCE) loss is applied in training while an adaptive thresholding is used in inference to remove unnecessary voxels and reduce the distortion. Objectively, our method exceeds the geometry-based point cloud compression (G-PCC) algorithm standardized by well-known Moving Picture Experts Group (MPEG) with a significant performance margin, e.g., at least 60% BD-Rate (Bjontegaard Delta Rate) gains, using common test datasets. Subjectively, our method has presented better visual quality with smoother surface reconstruction and appealing details, in comparison to all existing MPEG standard compliant PCC methods. Our method requires about 2.5MB parameters in total, which is a fairly small size for practical implementation, even on embedded platform. Additional ablation studies analyze a variety of aspects (e.g., cube size, kernels, etc) to explore the application potentials of our learned-PCGC.Comment: 13 page

Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling

This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure. Our NLAIC 1) embeds non-local network operations as non-linear transforms in the encoders and decoders for both the image and the latent representation probability information (known as hyperprior) to capture both local and global correlations, 2) applies attention mechanism to generate masks that are used to weigh the features, which implicitly adapt bit allocation for feature elements based on their importance, and 3) implements the improved conditional entropy modeling of latent features using joint 3D convolutional neural network (CNN)-based autoregressive contexts and hyperpriors. Towards the practical application, additional enhancements are also introduced to speed up processing (e.g., parallel 3D CNN-based context prediction), reduce memory consumption (e.g., sparse non-local processing) and alleviate the implementation complexity (e.g., unified model for variable rates without re-training). The proposed model outperforms existing methods on Kodak and CLIC datasets with the state-of-the-art compression efficiency reported, including learned and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics.Comment: arXiv admin note: substantial text overlap with arXiv:1904.0975