To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:mea...To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:measurement subrate(S) and quantization depth(D).By learning how different parameter combinations may affect the quality-bitrate characteristics of aerial images,two parameter allocation models are derived between a bitrate budget and its appropriate parameters.Based on the corresponding allocation models,a model-guided image coding method is proposed to pre-determine the appropriate(S,D) combination for acquiring an aerial image via QBCS.The data-driven experimental results show that the proposed method can achieve near-optimal quality-bitrate performance under the QBCS framework.展开更多
基金supported by the Natural Science Foundation of Shanghai(18ZR1400300)
文摘To progressively provide the competitive rate-distortion performance for aerial imagery,a quantized block compressive sensing(QBCS) framework is presented,which incorporates two measurement-side control parameters:measurement subrate(S) and quantization depth(D).By learning how different parameter combinations may affect the quality-bitrate characteristics of aerial images,two parameter allocation models are derived between a bitrate budget and its appropriate parameters.Based on the corresponding allocation models,a model-guided image coding method is proposed to pre-determine the appropriate(S,D) combination for acquiring an aerial image via QBCS.The data-driven experimental results show that the proposed method can achieve near-optimal quality-bitrate performance under the QBCS framework.
