In this paper, we propose a novel image recompression frame- work and image quality assessment （IQA） method to efficiently recompress Internet images. With this framework image size is significantly reduced without ...In this paper, we propose a novel image recompression frame- work and image quality assessment （IQA） method to efficiently recompress Internet images. With this framework image size is significantly reduced without affecting spatial resolution or perceptible quality of the image. With the help of IQA, the relationship between image quality and image evaluation scores can be quickly established, and the optimal quality factor can be obtained quickly and accurately within a pre - determined perceptual quality range. This process ensures the image＇s perceptual quality, which is applied to each input image. The test results show that, using the proposed method, the file size of images can be reduced by about 45%-60% without affecting their visual quality. Moreover, our new image -reeompression framework can be used in to many different application scenarios.展开更多
Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lo...Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lot of internet applications always focus on how to achieve appropriate image reeompression. In this paper, we propose a novel framework to efficiently customize image recompression according to a variety of applications. First of all, we evaluate the input image＇s compression level and predict an initial compression level which is very close to the final output of our system using a prior learnt from massive images. Then, we iteratively recompress the input image to different levels and measure the perceptual similarity between the input image and the new result by a block-based coding quality method. According to the output of the quality assessment method, we can update the target compression level, or switch to the subjective evaluation, or return the final recompression result in our system pipeline control. We organize subjective evaluations based on different applications and obtain corresponding assessment report. At last, based on the assessment report, we set up a series of appropriate parameters for customizing image recompression. Moreover, our new framework has been successfully applied to many commercial applications, such as web portals, e-commerce, online game, and so on.展开更多
基金supported in part by China"973"Program under Grant No.2014CB340303
文摘In this paper, we propose a novel image recompression frame- work and image quality assessment （IQA） method to efficiently recompress Internet images. With this framework image size is significantly reduced without affecting spatial resolution or perceptible quality of the image. With the help of IQA, the relationship between image quality and image evaluation scores can be quickly established, and the optimal quality factor can be obtained quickly and accurately within a pre - determined perceptual quality range. This process ensures the image＇s perceptual quality, which is applied to each input image. The test results show that, using the proposed method, the file size of images can be reduced by about 45%-60% without affecting their visual quality. Moreover, our new image -reeompression framework can be used in to many different application scenarios.
基金supported by a joint project of Tencent Research and Shanghai Jiao Tong Universitysupported by the National Basic Research 973 Program of China under Grant No.2011CB302203+2 种基金the National Natural Science Foundation of China under Grant Nos.61073089,61133009the Open Projects Program of National Laboratory of Pattern Recognition of Chinathe Open Project Program of the State Key Lab of CAD&CG of Zhejiang University of China under Grant No.A1206
文摘Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lot of internet applications always focus on how to achieve appropriate image reeompression. In this paper, we propose a novel framework to efficiently customize image recompression according to a variety of applications. First of all, we evaluate the input image＇s compression level and predict an initial compression level which is very close to the final output of our system using a prior learnt from massive images. Then, we iteratively recompress the input image to different levels and measure the perceptual similarity between the input image and the new result by a block-based coding quality method. According to the output of the quality assessment method, we can update the target compression level, or switch to the subjective evaluation, or return the final recompression result in our system pipeline control. We organize subjective evaluations based on different applications and obtain corresponding assessment report. At last, based on the assessment report, we set up a series of appropriate parameters for customizing image recompression. Moreover, our new framework has been successfully applied to many commercial applications, such as web portals, e-commerce, online game, and so on.
