Abstract
In traditional compressed sensing (CS), the measurement matrix always faces problems such as large data storage, high memory usage and a large amount of data calculation. To reduce the storage space of the measurement matrix, improve memory usage and reduce the amount of data, this paper proposes a CS strategy based on a semitensor product (STP). The STP is employed to sample a sparse matrix from the measurement matrix, and Arnold scrambling is used to increase the security of the secret image. Furthermore, a visual security strategy for the secret image is employed to transform it into a visually meaningful image. The simulations show that the proposed visual security strategy based on STP-CS can not only greatly reduce the storage space occupied by the measurement matrix but also maintain high reconstruction quality compared with the traditional CS model while ensuring visual security quality.
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