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Image compression approaches based on deep learning have achieved remarkable success.
Existing studies mainly focus on human vision and machine analysis tasks taking reconstructed images as input.

Block-based compressive imaging enables new video acquisition methodology while reducing raw data size, theoretically eliminating the need for complex coding algorithms. When transmitting raw data, however, the redundancy associated with random projection remains. This paper takes a fresh look at raw data structure by viewing it as cube made up of multiple downsampled images rather than a vector. As a result, we can view each individual data point as a pixel, allowing us to code more flexibly and versatility than state-of-the-art works.