Beyond Coding: Detectiondriven Image Compression with Semantically Structured Bitstream With the development of 5G and edge computing, it is increasingly important to offload intelligent media computing to edge device. Traditional media coding scheme codes the media into one binary stream without a semantic structure, which prevents many important intelligent applications from operating directly in bitstream level, including semantic analysis, parsing specific content, media editing, etc. Therefore, in this paper, we propose a learning based Semantically Structured Coding (SSC) framework to generate Semantically Structured Bitstream (SSB), where each part of bitstream represents a certain object and can be directly used for aforementioned tasks. Specifically, we integrate an object detection module in our compression framework to locate and align the object in feature domain. After applying quantization and entropy coding, the features are reorganized according to detected and aligned objects to form a bitstream. Besides, different from existing learningbased compression schemes that individually train models for specific bitrate, we share most of model parameters among various bitrates to significantly reduce model size for variablerate compression. Experimental results demonstrate that only at the cost of negligible overhead, objects can be completely reconstructed from partial bitstream. We also verified that classification and pose estimation can be directly performed on partial bitstream without performance degradation.

Lossless and nearlossless image compression with successive refinement We present a technique that provides progressive transmission and nearlossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image justlike what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides nearlossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. We formulate the image data compression problem as one of asking the optimal questions to determine, respectively, the value or the interval of the pixel, depending on whether one is interested in lossless or nearlossless compression. New prediction methods based on the nature of the data at a given pass are presented and links to the existing methods are explored. The tradeoff between noncausal prediction and data precision is discussed within the context of successive refinement. Context selection for prediction in different passes is addressed. Finally, experimental results for both lossless and nearlossless cases are presented, which are competitive with the stateoftheart compression schemes.