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公开(公告)号：US09842387B2
公开(公告)日：2017-12-12
申请号：US14743633
申请日：2015-06-18
Applicant: VMware, Inc.
Inventor： Uday Kurkure , Hari Sivaraman , Peter Farkas , Xingze He
CPC classification number: G06T7/0002 , G06T2207/10024 , G06T2207/20221 , G06T2207/30168
Abstract: Techniques disclosed herein provide an approach using pixel perturbation to measure image quality. In one embodiment, a pixel perturbation engine perturbs pixels in an image with a reference image for measuring quality of the image after transmission. By perturbing least significant bits, a composite image may be generated in which the reference image is hidden in the original image. The perturbations in the composite image may then be recovered after the composite image is transmitted to a remote device and used to determine image quality based on preservation of the perturbations. In another embodiment, image(s) perturbed with reference image(s) at increasingly higher order bit positions may be transmitted, and quality of the transmitted reference image determined, until the determined quality exceeds a threshold, with the perturbed bit position at which the determined quality exceeds the threshold being indicative of the quality of the image(s) transmitted.
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公开(公告)号：US10255667B2
公开(公告)日：2019-04-09
申请号：US15213445
申请日：2016-07-19
Applicant: VMWARE, INC.
Inventor： Xingze He , Banit Agrawal , Rishi Bidarkar , Hari Sivaraman , Uday Kurkure
Abstract: Techniques are described for improving the measurement of visual perception of graphical user interface (GUI) information remoted to client devices in virtual desktop environments, such as VDI and DAAS. An objective image quality measurement of remoted virtual desktop interfaces is computed, that is more accurate and more closely aligned with subjective user perception. The visual quality metric is computed using a linear fusion model that combines a peak signal to noise ratio (PSNR) score of the distorted image, a structural similarity (SSIM) score of the distorted image and a feature similarity (FSIM) score of the distorted image. Prior to using the model to compute the quantitative visual perception metric, the linear fusion model is trained by using a benchmark test database of reference images (e.g., virtual desktop interface images), distorted versions of those images and subjective human visual perception quality ratings associated with each distorted version.
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公开(公告)号：US09866724B2
公开(公告)日：2018-01-09
申请号：US14743580
申请日：2015-06-18
Applicant: VMware, Inc.
Inventor： Uday Kurkure , Hari Sivaraman , Peter Farkas , Xingze He
CPC classification number: H04N1/32219 , G06T1/0028 , G06T7/0002 , G06T2201/0051 , G06T2201/0081 , G06T2201/0202 , G06T2207/10024 , G06T2207/30168 , H04N1/32277 , H04N1/32309
Abstract: Techniques disclosed herein provide an approach using pixel perturbation to transmit information. In one embodiment, a pixel perturbation engine perturbs pixels of an image to encode meta-information therein. In particular, the perturbation engine may perform controlled perturbation of pixel bits which are unlikely to affect visual quality are perturbed, such as perturbing the least significant bits in one or more channels of a RGB or YUV image. Higher order bits may also be perturbed in color channels such as the blue or UV channels that the human eye is less sensitive to. To mitigate loss of information, the information may be encoded using error correcting codes. After the image having meta-information encoded therein is transmitted to a client device, the meta-information may be recovered and used to determine performance of the virtual desktop infrastructures (VDI) system.
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