Secure Image Block Compressive Sensing Using Chaotic DCT Sparse Basis and Partial Chaotic DHT Measurement Matrix

IET Information Security

2022

Self Cite

This paper proposes a novel image compression-encryption scheme based on compressive sensing and bit-level XOR. In the proposed scheme, the encrypted sequences are generated from a 4-D hyper-chaotic Lorenz system and a Logistic system. First, an original image is sampled by a secure block parallel compressive sensing (PCS) scheme. In the PCS phase, a key-controlled discrete cosine transform sparse basis matrix and a key-controlled complex Hadamard measurement matrix are employed. Next, the real part and imaginary part of the resulting complex-valued data are quantized and transformed into bit streams, respectively. After that, the two generated bit streams are combined into one bit stream. Then, the resulting bit stream is further encrypted by a bitlevel XOR operation, where the encrypted key is generated from a chaotic system. The experiment results show the effectiveness and reliability of the proposed joint compression-encryption scheme. The proposed scheme can not only enhance the security of the compressed image but also improve the reconstructed quality of the compressed image.

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Performance Of the Proposed Partial Complex Hadamard Measure...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure image block compressive sensing using complex Hadamard measurement matrix and bit‐level XOR

Xue

IET Information Security

2022

Self Cite

“…The measurement matrix plays a vital role in reconstructed PSNR performance. To evaluate the performance of our CS method, we compare the used measurement matrix with other exiting measurement matrices, namely, random matrix, Toeplitz matrix   2 10 10log 255 / PSNR MSE  10 [28], chaotic logistic matrix [29], partial chaotic Hadamard matrix [30], and partial DHT matrix [31]. The size of measurement matrix is 16 M  , where M denotes the row number of measurements and 16 M  .…”

Section: Effect Of Different Measurement Matrices On the Psnr Of Bcsmentioning

confidence: 99%

Compressed image transmission in precoded OFDM VLC systems

Hou

2022

Preprint

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In this paper, a compressed image transmission scheme is proposed in a precoded orthogonal frequency division multiplexing (OFDM)-based visible light communication (VLC) system. First, an image is divided into sub-block images; then, each block image is sampled by block-based compressive sensing (BCS) based on the Hadamard sparse basis matrix with a partial DCT measurement matrix. The resulting data are transformed into bit stream, which is transmitted in a DFT precoded OFDM-based VLC. We propose an integrated end-to-end OFDM-based VLC system platform, including BCS, quantization, VLC link, and OFDM transmission. The simulation results prove the effectiveness of the proposed simulation platform. The bit error rate (BER) and peak signal-to-noise ratio (PSNR) performances of the simulation platform are evaluated as well. The simulation results show that different images result in different BER and PSNR performances in the proposed precoded OFDM-VLC system. The larger the information entropy of images, the better the PSNR and BER performances. In addition, the PAPR of the DFT precoded OFDM can be significantly reduced compared to that of the conventional OFDM for compressed image data.

Image parallel block compressive sensing scheme using DFT measurement matrix

Jiang

Chen

2022

Multimed Tools Appl

Self Cite

Compressive sensing (CS)-based image coding has been widely studied in the field of image processing. However, the CS-based image encoder has a significant gap in image reconstruction performance compared with the conventional image compression methods. In order to improve the reconstruction quality of CS-based image encoder, we proposed an image parallel block compressive sensing (BCS) coding scheme, which is based on discrete Cosine transform (DCT) sparse basis matrix and partial discrete Fourier transform (DFT) measurement matrix. In the proposed parallel BCS scheme, each column of an image block is sampled by the same DFT measurement matrix. Due to the complex property of DFT measurement matrix, the compressed image data is complex. Then, the real part and imaginary part of the resulting BCS data are quantized and transformed into two bit streams, respectively. At the reconstruction stage, the resulting two bit streams are transformed back into two real signals using inverse quantization operation. The resulting two real signals are combined into one complex signal, which is served as the input data of the CS reconstructed algorithm. The theoretical analysis based on minimum Frobenius norm method demonstrates that the proposed DFT measurement matrix outperforms the other conventional measurement matrices. The simulation results show that the reconstructed performance of the proposed DFT measurement matrix is better than that of the other conventional measurement matrices for the proposed parallel BCS. Specifically, we analyzed the impact of quantization on the reconstruction performance of CS. The experiment results show that the effect of the quantization on reconstruction performance in BCS framework can nearly be ignored.