公开(公告)号：US20220358537A1

公开(公告)日：2022-11-10

申请号：US17760535

申请日：2020-08-06

Applicant: Google LLC

Inventor： Catherine Shyu ,  Luying Li ,  Feng Yang ,  Junjie Ke ,  Xiyang Luo ,  Hao Feng ,  Chao-Hung Chen ,  Wenjing Kang ,  Zheng Xia ,  Shun-Chuan Chen ,  Yicong Tian ,  Xia Li ,  Han Ke

IPC: G06Q30/02

Abstract: Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.

公开(公告)号：US11178430B2

公开(公告)日：2021-11-16

申请号：US16210900

申请日：2018-12-05

Applicant: Google LLC

Inventor： Peyman Milanfar ,  Feng Yang ,  Sungjoon Choi

IPC: H04N19/625 ,  H04N19/124 ,  H04N19/184

Abstract: Methods are provided for sharpening or otherwise modifying compressed images without decompressing and re-encoding the images. An overall image quality is determined based on the source of the compressed image, the quantization table of the compressed image, or some other factor(s), and a set of scaling factors corresponding to the image quality is selected. The selected scaling factors are then applied to corresponding quantization factors of the image's quantization table or other parameters of the compressed image that describe the image contents of the compressed image. The scaling factors of a given set of scaling factors can be determined by a machine learning process that involves training the scaling factors based on training images determined by decompressing and then sharpening or otherwise modifying a source set of compressed images. These methods can provide improvements with respect to encoded image size and computational cost of the image modification method.