Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0400001(2023)

Advances in Optical Image Compression and Encryption Methods

Yi Qin1,2, Tianlong Man1, Yuhong Wan1、*, and Xing Wang2
Author Affiliations
  • 1Faculty of Science, Beijing University of Technology, Beijing 100124, China
  • 2College of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang 473061, Henan, China
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    Figures & Tables(45)
    Optical implementation of double random phase encoding. (a) Encryption; (b) decryption
    Information processing flowchart for different compression strategies in optical compression-encryption system. (a) Plaintext compression; (b) ciphertext compression; (c) synchronized compression
    An example of information compression by using the concentration of energy in the transform domain
    Principle of G-S algorithm
    Single pixel camera based on compressive sensing[41]
    Double-image encryption based on frequency spectral fusion[44]
    Simulation results of multiple-image encryption based on frequency spectral fusion[44]. (a) Original images; (b) ciphertext; (c) decrypted images
    Multiple-image compression and encryption based on Radon transform[52]
    Image encryption and decryption process based on compression sensing and dual random phase coding system[55]
    Simulation results of image encryption system based on compression sensing and double random phase coding[55]. (a) Original image; (b) image downsampled by sensing matrix; (c) host image; (d) ciphertext; (e) combined image containing cipher information; (f) reconstructed image
    Schematic of optical encryption process based on spatial multiplexing and compression sensing[58]
    Schematic of optical decryption process based on spatial multiplexing and compression sensing[58]
    Multiple-image encryption based on position multiplexing[63]. (a) Encryption; (b) decryption
    Numerical simulation results of multiple-image encryption scheme based on position multiplexing [63]. (a) Ciphertext; (b) decryption corresponding to position d5; (c) result of Gaussian filtering on the image shown in Fig. 14 (b); (d) decryption corresponding to position d1-3.5Δdmin
    Theta-modulation-based multiple-image encryption[71]
    Reconstruction of ciphertexts in theta-modulation-based multiple-image encryption[71]
    Reconstruction of plaintexts in theta-modulation-based multiple-image encryption[71]
    Multiple-image encryption based on angular multiplexing of CCD[75]
    Spectrum of the synthetic ciphertextofmultiple-image encryption based on angular multiplexing of CCD[75]. (a) Simulation result; (b) experimental result
    Decrypted results obtained by quantizing each pixel value in the ciphertext by different orders[30]. (a) 4 bits; (b) 3 bits; (c) 2 bits
    Optical ciphertext compression method based on deep learning[82]. (a) Compression; (b) decompression
    Comparison of the deep-learning-based optical ciphertext compression approach with JPEG and JPEG2000[82]
    Optical encryption based on compressive ghost imaging encryption[29]
    Decrypted results using compressive ghost imaging[29]. (a) Plaintext; (b) decrypted result obtained by conventional method under 3500 samplings; (c) decrypted result obtained by compressive sensing under 3500 samplings; (d) decrypted result obtained by compressive sensing under 200 samplings
    Encryption system based on single pixel imaging, phase shifting holography, and random phase coding[90]
    Decryption result of gray image obtained by encryption system[90]. (a) Plaintext; (b) one of the encrypted holograms on the DMD plane; (c) retrieved image of about 256×256×42.1% measurements, where 256×256 denotes the pixel count and 42.1% denotes the sampling ratio
    Optical decryption scheme of multi-image encryption system based on multi-plane phase recovery and interference principle[93]
    Iterative algorithm of multi-image encryption system based on multi-plane phase recovery algorithm and interference principle[93]
    Multiple-image encryption based on 3D space and phase retrieval algorithm[94]
    Multiple-image encryption based on azimuth multiplexing and phase retrieval algorithm[95]
    Iterative cryptosystem based on amplitude constraint in input plane[97]. (a) Decryption optical path and iterative algorithm basis; (b) amplitude constraint in input plane; (c) amplitude constraint in output plane
    Ciphertext combination method based on spatial multiplexing[97]
    Optical encryption based on 4f correlator[98]
    Secret sharing (multiple-image encryption) system based on metasurface and iterative algorithm[33]
    Optical diffractive-imaging-based encryption scheme
    Effect of decryption algorithm of single exposure optical diffraction imaging encryption system[107]. (a) Decrypted image; (b) dependence of CC on iteration number; (c) dependence of CC on iteration number corresponding to the first iterative procedure; (d) dependence of CC on iteration number corresponding to the second iterative procedure
    Multi-image encryption system based on multimode phase retrieval algorithm and focal length multiplexing[110]
    Relationship between the quality of the decrypted images (CC) and the iteration number in the multi-image encryption system based on multimode phase retrieval algorithm and focal length multiplexing[110]
    Single exposure color image encryption system based on multimodal diffraction imaging[111]
    Multiple-image encryption based on compressive holography[114]
    Decrypted results of multiple-image encryption based on compressive holography[114]. (a)-(c) Plaintexts; (d) one of the holograms; (e)-(g) decrypted results
    • Table 1. Compression strategies and methods for optical image compression-encryption

      View table

      Table 1. Compression strategies and methods for optical image compression-encryption

      Compression strategyCompression method
      Plaintext compressionTransform domain compression
      Compressive sensing
      Ciphertext compressionParameter multiplexing compression
      Classical compression
      Compressive sensing
      Synchronized compressionIterative phase retrieval algorithm
      Compressive sensing
    • Table 2. Results by applying several classical compression methods to the original ciphertext[30]

      View table

      Table 2. Results by applying several classical compression methods to the original ciphertext[30]

      Hol.no.Size(kB)LZ77(kB)LZW(kB)Huff.(kB)BW(kB)Compression ratio
      LZ77LZWHuff.BW
      165,53662,65165,53662,52963,8691.051.001.051.03
      265,53662,64465,53662,51963,8361.051.001.051.03
      365,53662,64565,53662,51563,8231.051.001.051.03
      465,53662,64365,53662,51563,8251.051.001.051.03
      565,53662,64165,53662,51363,8251.051.001.051.03
      Averages:1.051.001.051.03
    • Table 3. Results by applying several classical compression methods to the quantized ciphertext[30]

      View table

      Table 3. Results by applying several classical compression methods to the quantized ciphertext[30]

      BitsSize(kB)LZ77(kB)LZW(kB)Huff.(kB)BW(kB)Compression ratio
      LZ77LZWHuff.BW
      265,536(16,384)47421027321394(349)1560(390)64(16)2048(512)
      365,536(16,384)113810061317109758(14)65(16)50(12)60(15)
      465,536(16,384)212019631991208431(7.7)33(8.3)33(8.2)31(7.9)
      565,536(16,384)309729693021298521(5.3)22(5.5)22(5.4)22(5.5)
      665,536(16,384)400340183923390116(4.1)16(4.1)17(4.2)17(4.2)
      765,536(16,384)473251244784479514(3.5)13(3.2)14(3.4)14(3.4)
      865,536(16,384)546062365613565912(3.0)11(2.6)12(2.9)12(2.9)
    • Table 4. Comparison and analysis of the aforementioned compression methods

      View table

      Table 4. Comparison and analysis of the aforementioned compression methods

      Compression strategyCompression methodFrameAdvantages and disadvantages
      Plaintext compressionTransform domain compressionThis method always offers high quality decryption,but the independence between the compression/decompression and the encryption/decryption leads to time consumption.
      Compressive sensingThis method always offers high quality decryption,but the decompression is time-consuming;meanwhile,the independence between the compression/decompression and the encryption/decryption leads to time consumption.
      Ciphertext compressionParameter multiplexing compressionThis method always suffers from low-quality decrypted results caused by cross-talk noise,but the decompression and decryption are always carried out simultaneously with a pure optical manner. Some preprocessing or postprocessing approaches can be adopted to alleviate the cross-talk noise at the cost of time.
      Classical compressionThe independence between the compression/decompression and the encryption/decryption leads to time consumption. The quality of the decryption will seriously degrade in the case of a high compression ratio;however,deep learning provides a new avenue for coping with such issues.
      Compressive sensingThis method enables simultaneously compression and encryption,and it is widely used in cryptosystems based on ghost/single-pixel imaging. This method can always achieve a high compression ratio,but the decryption(decompression)is time-consuming.
      Synchronized compressionIterative phase retrieval algorithmIteratively encryption,optically decryptionThe encryption procedure is time-consuming,but the decryption(decompression)can always be performed optically. The quality of the decrypted images is relatively high.
      Optically encryption,iteratively decryptionThe encryption procedure can always be performed optically,but the decryption(decompression)procedure is time-consuming. The quality of the decrypted images is relatively high.
      Compressive sensingThis method enables simultaneously compression and encryption with a pure optical manner,but the decryption(decompression)procedure is time-consuming.
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    Yi Qin, Tianlong Man, Yuhong Wan, Xing Wang. Advances in Optical Image Compression and Encryption Methods[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0400001

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    Paper Information

    Category: Reviews

    Received: May. 17, 2022

    Accepted: Jul. 14, 2022

    Published Online: Feb. 14, 2023

    The Author Email: Wan Yuhong (yhongw@bjut.edu.cn)

    DOI:10.3788/LOP221626

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