DeepSIC: Deep Semantic Image Compression
نویسندگان
چکیده
Incorporating semantic information into the codecs during image compression can significantly reduce the repetitive computation of fundamental semantic analysis (such as object recognition) in client-side applications. The same practice also enable the compressed code to carry the image semantic information during storage and transmission. In this paper, we propose a concept called Deep Semantic Image Compression (DeepSIC) and put forward two novel architectures that aim to reconstruct the compressed image and generate corresponding semantic representations at the same time. The first architecture performs semantic analysis in the encoding process by reserving a portion of the bits from the compressed code to store the semantic representations. The second performs semantic analysis in the decoding step with the feature maps that are embedded in the compressed code. In both architectures, the feature maps are shared by the compression and the semantic analytics modules. To validate our approaches, we conduct experiments on the publicly available benchmarking datasets and achieve promising results. We also provide a thorough analysis of the advantages and disadvantages of the proposed technique.
منابع مشابه
A Hybrid Algorithm based on Deep Learning and Restricted Boltzmann Machine for Car Semantic Segmentation from Unmanned Aerial Vehicles (UAVs)-based Thermal Infrared Images
Nowadays, ground vehicle monitoring (GVM) is one of the areas of application in the intelligent traffic control system using image processing methods. In this context, the use of unmanned aerial vehicles based on thermal infrared (UAV-TIR) images is one of the optimal options for GVM due to the suitable spatial resolution, cost-effective and low volume of images. The methods that have been prop...
متن کاملNear-lossless L-infinity constrained Multi-rate Image Decompression via Deep Neural Network
Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decompressed patches to the original counterparts under the ubiquitous `2 metric. However, this approach is incapable of restoring distinctive image details which may be statistical outliers but have high semantic importan...
متن کاملSemantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models
متن کامل
Semiautomatic Image Retrieval Using the High Level Semantic Labels
Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...
متن کاملSemantic image compression based on data hiding
This study proposes a novel scheme of semantic image compression. A compressor firstly creates a compact image by gathering a part of pixels in an original image, and calculates estimation errors of the rest pixels. Then, a compressed image is produced by embedding the estimation errors into the compact image using data hiding techniques. This way, the compressed image are made up of a small nu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1801.09468 شماره
صفحات -
تاریخ انتشار 2018