Offline and Online Optical Flow Enhancement for Deep Video Compression

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion trade-off. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side.Comment: 9 pages, 6 figure

Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Impacts of a wildfire on soil organic carbon in Warrumbungle National Park, Australia

A wildfire in the Warrumbungle Range in January 2013 burnt 56,290 ha of forest land, 72% of it at high-extreme severity. We investigated the effects of fire on soil organic carbon (SOC), soil carbon fractions (Particulate Organic Carbon (POC), Humus Organic Carbon (HOC) and Resistant Organic Carbon (ROC)) at 64 sites stratified according to geology and fire severity across Warrumbungle National Park. Statistical models were used to identify the main factors controlling the soil chemical parameters and we spatially extrapolated results based on these main factors to estimate the overall impacts of the fire. Statistical models indicated that the key effects on SOC were fire severity and geology/soil type. SOC declined with increasing fire severity − topsoil SOC in low severity sites was 14% lower than unburnt sites, and severely burnt sites were 54% lower than unburnt. There were also significant differences in SOC fractions between the different geology/soil types. These results were also reflected in N and pH changes. The highest SOC values were from unburnt volcanic topsoils. Sandier and especially sandstone-derived soils had less SOC irrespective of the fire severity class. The lowest SOC values were from severely burnt sandstone ridges, where most of the remaining SOC occurs as ROC (including charcoal). Site data was classified according to a fire severity map and geological mapping, and class averages spatially extrapolated to obtain an estimate of the amounts of SOC lost due to the fire. An estimated 1.52 Mt (26.99 t/ha) of SOC was lost over the fire ground to 10 cm. SOC levels in unburnt control sites are much higher than averages in the generally cleared central west of NSW, thus underlining the importance of forested ecosystems in carbon sequestration in soils, and of Warrumbungle National Park with its high proportion of trachytic clayey soils in particular

Determination of Camellia oleifera Abel. Germplasm Resources of Genetic Diversity in China using ISSR Markers

Camellia oleifera is one of the four woody oil plants in the world, which is widely cultivated in South China. To examine the genetic diversity of C. oleifera in China, the diversity and genetic relationships among and within major populations of 109 varieties of C. oleifera were analyzed using ISSR markers. Twenty-three ISSR primers out of 49 primers yielded approximately 487 legible bands. A total of 335 of these bands were polymorphic markers, and the ratio of polymorphism was 68.86%. From the results, Zhejiang province showed the highest populations genetic diversity (H value 0.18), while Guangxi population showed the lowest genetic diversity (H 0.0851). Base on the bands, the genetic similarity coefficient ranged from 0.61 to 0.93 using NTSYS2.10e software. When coefficient was 0.75, 109 cultivars were divided into 11 categories and categories I contain 79 varieties by UPGMA cluster analysis. The test varieties divided into 7 sub-groups when categories were 0.75, which show a close genetic relationship. Results advised that Hunan is the main producing area of C. oleifera, with enriched C. oleifera variety and complex topography, and therefore has a high genetic diversity. Meanwhile, the main varieties of C. oleifera in Hubei are imported from Hunan, which results in fewer varieties and reduces the genetic diversity of C. oleifera. The ISSR profiles can improve C. oleifera germplasm management and provide potential determine correlations between different varieties and its distribution in different province

Rural and urban land tourism and destination image: a dual-case study approach examining energy-saving behavior and loyalty

Although the significance of destination image is acknowledged, its effect on tourist reactions, especially energy-saving behavior, remains unknown. This research aimed to explore tourist energy-saving behavior (TESB) and loyalty (TL) in a rural land context by using the cognition affect-behavior (CAB) model. The findings indicated: (1) destination image positively and directly influenced TESB and TL; (2) relationship quality variables, i.e., tourist satisfaction and destination trust, positively and separately mediated the associations of destination image with TESB and TL; and (3) a cross-validation approach of rural and urban cases documented support for the research findings. This study extends the destination image literature by introducing the CAB model and the cross-validation approach to examine energy-saving behavior and loyalty. It offers guidance and a reference for tourism destination practitioners to promote energy-saving behavior and loyalty through the enhancement of destination image and relationship quality

Landscape and unique fascination: a dual-case study on the antecedents of tourist pro-environmental behavioral intentions

Although cultivating tourist pro-environmental behavioral intentions (TPEBI) has been emphasized, the effect of destination unique fascination on TPEBI is unknown. Applying the theory of planned behavior (TPB) and the cognition-affect-behavior (CAB) model, this research develops an integrated theoretical framework to predict TPEBI. The results suggested that: (1) attitudes toward the behavior, subjective norms, perceived behavioral control, destination unique fascination and tourist delight directly influence TPEBI; (2) tourist delight positively meditates the links between destination unique fascination and TPEBI; (3) the integrated model had better explanation power than either TPB or CAB models; and (4) a cross-validation method of rural and wetland cases demonstrated support for the results. This study enriches the extant studies of pro-environmental behavioral intentions by introducing an integrated conceptual model coupled with the cross-validation approach. Aside from the impact of TPB constructs, the research offers a reference for practitioners to promote TPEBI through the enhancement of destination unique fascination and tourist delight

Predicting private and public pro-environmental behaviors in rural tourism contexts using SEM and fsQCA: the role of destination image and relationship quality

The importance of pro-environmental behavior in tourism has been established, but explaining its sub-dimensions, especially in the private and public dimensions, is under-researched. Existing literature on tourism research mainly uses SEM to analyze tourist pro-environmental behavior, while fsQCA is scarcely implemented. In this study, SEM is applied to reveal the links among destination image, relationship quality, and pro-environmental behavior, while fsQCA is utilized to investigate configurations predicting pro-environmental behavior. Responses of 285 tourists were collected and analyzed to test the proposed hypotheses. The SEM results showed that (1) destination image directly and positively affected relationship quality (including satisfaction and destination trust); (2) relationship quality was found to positively and directly influence private and public pro-environmental behaviors; (3) relationship quality did mediate the influence of destination image on private pro-environmental behavior partially, while it played a full mediating role in the effect of destination image on public pro-environmental behavior. The findings from fsQCA indicated that (1) three sufficient configurations consistently lead to a high level of private pro-environmental behavior: (a) high destination image and satisfaction, (b) high destination image and trust, (c) high relationship quality; (2) there was only one sufficient causal configuration for a high level of public pro-environmental behavior: high relationship quality. The results provide tenable evidence that relationship quality can be a vital factor enhancing the sub-dimensions of pro-environmental behavior. The integration of these two methods helps to open the black box of tourist pro-environmental behavior in rural tourism contexts in a more systematic and holistic way