This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In\nblock compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag\nscrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via\ncompressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding\noperations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings\nhave a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a\nlandscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the lowfrequency\nand high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is\nsimultaneously applied to the secret image as well to split it. Next, it is embedded into the DCTcoefficients of the low-frequency\nand high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under\nthe same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger\nsecurity and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements\nthan the existing ones.
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