A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page load process to improve responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore an active research direction. In this work, we concentrate on extreme compression rates, where the size of the image is typically 200 bytes or less. First, we propose a novel approach for image compression that, unlike commonly used methods, does not rely on block-based statistics. We use an approach based on an adaptive triangulation of the target image, devoting more triangles to high entropy regions of the image. Second, we present a novel algorithm for encoding the triangles. The results show favorable statistics, in terms of PSNR and SSIM, over both the JPEG and the WebP standards.
. (2011) 'Links between notchback geometry, aerodynamic drag, ow asymmetry and unsteady wake structure.', SAE International journal of passenger cars. Mechanical systems., 4 (1). pp. 156-165. Further information on publisher's website:http://dx.doi.org/10.4271/2011-01-0166Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTThe rear end geometry of road vehicles has a significant impact on aerodynamic drag and hence on energy consumption. Notchback (sedan) geometries can produce a particularly complex flow structure which can include substantial flow asymmetry. However, the interrelation between rear end geometry, flow asymmetry and aerodynamic drag has lacked previous published systematic investigation.This work examines notchback flows using a family of 16 parametric idealized models. A range of techniques are employed including surface flow visualization, force measurement, multi-hole probe measurements in the wake, PIV over the backlight and trunk deck and CFD.It is shown that, for the range of notchback geometries investigated here, a simple offset applied to the effective backlight angle can collapse the drag coefficient onto the drag vs backlight angle curve of fastback geometries. This is because even small notch depth angles are important for a sharp-edged body but substantially increasing the notch depth had little further impact on drag.This work shows that asymmetry originates in the region on the backlight and trunk deck and occurs progressively with increasing notch depth, provided that the flow reattaches on the trunk deck and that the effective backlight angle is several degrees below its crucial value for non-reattachment. A tentative mapping of the flow structures to be expected for different geometries is presented.CFD made it possible to identify a link between flow asymmetry and unsteadiness. Unsteadiness levels and principal frequencies in the wake were found to be similar to those for high-drag fastback geometries. The shedding of unsteady transverse vortices from the backlight recirculation region has been observed.
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited-and metered-bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page-load process. Recent work, based on an adaptive triangulation of the target image, has shown the ability to generate thumbnails of full images at extreme compression rates: 200 bytes or less with impressive gains (in terms of PSNR and SSIM) over both JPEG and WebP standards. However, qualitative assessments and preservation of semantic content can be less favorable. We present a novel method to significantly improve the reconstruction quality of the original image with no changes to the encoded information. Our neural-based decoding not only achieves higher PSNR and SSIM scores than the original methods, but also yields a substantial increase in semantic-level content preservation. In addition, by keeping the same encoding stream, our solution is completely inter-operable with the original decoder. The end result is suitable for a range of small-device deployments, as it involves only a single forward-pass through a small, scalable network.
Enormous success has been achieved with deep neural networks consisting of standard linear-convolutions followed by simple non-linear mapping functions. In this paper, we add easily-computed non-linear local and global statistics to deep architectures, augmenting the information available at each layer. This additional information is then used in an identical manner to current processing. The summary statistics, which can be as simple as calculating within-channel variance, introduces little run-time computational overhead and can be instantiated with few extra parameters. All standard training procedures can be used without modification for training these augmented networks. We show, through extensive testing with ResNet on Im-ageNet, performance improvements across a wide range of network sizes. Additionally, we provide a detailed study of where within the deep networks these statistics are most effective.
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