We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm, while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform and high-frequency minimization encoding algorithm. The method involves compression-encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego-audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of signal-to-noise ratio and peak signal-to-noise ratio.
In this paper, a novel method for 2D image compression is proposed and demonstrated through high-quality image reconstruction with compression ratios up to 99%. The proposed algorithm uses multiple divisions to divide an image into two different matrices: the number of division matrix and the reminder matrix. DCT is applied to these matrices to increase high-frequency coefficients. Then, the final coefficient matrices are encoded using Binary Matrix encoding Algorithm. This final new algorithm removes blocks of zeros and indexes them with only a “0”, while other blocks with nonzero coefficients are kept. At the decompression stage, the process starts with inverse Binary Matrix encoding, which returns all zeros at exact locations. The next step is inverse DCT, which is applied to retrieve the original matrices: the Number of Division matrix and the Reminder matrix. Finally, the image is decoded by combining the two retrieved matrices. The experimental results show that our method achieved high compression ratios up to 99% with better perceptual quality of reconstructed images compared to the popular JPEG method.
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