| In recent years,with the popularization of smart phones and the rapid development of the Internet,images uploaded to the Internet have presented geometric growth.Moreover,with the upgrading of smart devices,the size of images uploaded to the cloud is getting larger and larger,so these cloud images occupy more and more storage space.How to efficiently compress and store massive images appearing in the cloud has become an important research issue that needs urgent solution.Traditional image compression uses JPEG,MPEG,and other encoding methods.It uses techniques such as quantization and DCT transform to remove redundant information within the image.It does not consider the correlation between images,and the encoding efficiency cannot meet the current massive cloud image compression and storage.Group image encoding uses the correlation between images to encode,but group image encoding is generally calculate all images to get reference method and it is not applicable to the current cloud massive image compression.Therefore,in order to achieve efficient compression of massive images appearing in the cloud,an image coding scheme based on a cloud platform has proposed.This paper first introduces traditional image encoding and group image encoding,and then introduces an image coding scheme based on cloud platform.In this scheme,the input image is sampled to obtain a thumbnail with a smaller size,and then feature information is extracted from the input image.The obtained feature information and thumbnails are used to perform searching and matching in the cloud image database,and get the image block that similar to the input image.Finally,these image blocks for further processing to get the input image,so as to complete the entire cloud-based image encoding.In this paper,based on the study of image coding scheme based on cloud platform,an image coding algorithm based on cloud object database is proposed.The algorithm of this paper firstly obtains the basic cloud object database by selecting part of object images in the Image Net database and then extract the features from the object images.Then the feature of image is extracted from the object image detected by the to-be-encoded image,and search the matching objects in the cloud object database to obtain the reference object image,and perform dimensional unification,perspective transformation,and photometric conversion operations on reference object image.Finally,perform HEVC inter coding.When no object is detected in the to-be-encoded image or the reference object image is not obtained from the cloud object database,the encoding mode is switched to HEVC intra coding.After the coding of the to-be-encoded image is completed,the cloud object database is further perfected in real time by performing filtering and storing operations on the object of to-be-encoded image.This method uses the object information in the to-be-encoded image and the object information in the cloud object database to perform HEVC inter coding,which ensures objective fidelity,and when no object information is used,it provides a compensation encoding mechanism and switches to HEVC intra coding to ensure coding efficiency.By testing multiple images from cell phone shooting,Internet search,and Paris datasets,and calculating the average of the experimental results,compared with the HEVC intra coding method with higher efficiency in the existing image coding methods,the peak signal-to-noise ratio(PSNR)of the image coding algorithm based on a cloud object database can be increased by about 0.3 d B at the same bit rate.That is,the image coding algorithm based on the cloud object database improves the coding efficiency compared to the HEVC intra coding. |
