Abstract: 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.

Key words: block compressive sensing (BCS), measurement subrate, quantization depth, quality-bitrate, aerial image