As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested images. Our algorithm provides very important PSNR and MSSIM values for MRI images
<p><span>Channel coding for the fifth generation (5G) mobile communication is currently facing new challenges as it needs to uphold diverse emerging applications and scenarios. Massive machine-type communication (mMTC) constitute one of the main usage scenarios in 5G systems, which promise to provide low data rate services to a large number of low power and low complexity devices. Research on efficient coding schemes for such use case is still ongoing and no decision has been made yet. Therefore, This paper compares the performance of different coding schemes, namely: tail-biting convolutional code (TBCC), low density parity check codes (LDPC), Turbo code and Polar codes, in order to select the appropriate channel coding technique for 5G-mMTC scenario. The considered codes are evaluated in terms of bit error rate (BER) and block error rate (BLER) for short information block lengths (K ≤ 256). We further investigate their Algorithmic complexity in terms of the number of basic operations. The Simulation results indicate that polar code with CRC-aided successive cancelation list decoder has better performance compared with other coding schemes for 5G-mMTC scenario.</span></p>
<p>In the modern time interacting with digital world become standard life activity, human need a way to protect properties as individuals or corporals, and we do that by embedding a digital mark to the target, and this technique call digital watermarking. But there still is a chance to manipulate or even remove this marks we embed for protection with various attacks like adding noises, compression-decompression or bits manipulations, and that why companions, individuals, laboratories are still developing new methods to embed this marks and make them more robust and more hard to detect for others. There are so many methods for digital watermarking, so we chose the least significant bits watermarking (LSB-watermarking) to provide an invisible digital watermarking, and on top of that we proceed with the blind LSB-watermarking method so that we don't get bind to the original image, and for our attack we chose compression joint photographic experts group (JPEG) compression because it’s the most used method for image and videos compression along with singular value decomposition (SVD) to make our mark as robust as possible. And the results we gain from our method are promising and it did give as high quality digital watermarking.</p>
<p>The transmission of compressed data over wireless channel conditions represents a big challenge. The idea of providing robust transmission gets a lot of attention in field of research. In this paper we study the effect of the noise over wireless channel. We use the model of Gilbert-Elliot to represent the channel. The parameters of the model are selected to represent three cases of channel. As data for transmission we use images in gray level size 512x512. To minimize bandwidth usage we compressed the image with vector quantization also in this compression technique we study the effect of the codebook in the robustness of transmission so we use different algorithms to generate the codebook for the vector quantization finally we study the restoration efficiency of received image using filtering and indices recovery technique.</p>
In the past few years, many techniques have been explored to investigate the transmission of vector quantization (VQ) indices over noisy channels. In this paper, we propose an algorithm to correct or conceal the channel errors found in a VQ encoded Medical image. In the proposed method, some verification information (bloc mean and complexity variation) is embedded into the indices of the VQ encoded data. This information can help with the detection of errors in the indices transmitted over a noisy channel. Our method does not need to rearrange the codebook. In addition, a new error concealment technique is proposed to correct the erroneous image. According to our experiments, the proposed scheme provides a high detection result in the BER 0.01 noisy channel. Comparing to the related method, Anti-Gray Coding (AGC) and checksums, the proposing method performs better under BER 10 −3 -10 −2 . Moreover, our method does not have the codebook rearrangement that it is obligatory in AGC and other methods and need to more time. Therefore, our method is suitable to noisy and less noisy channels.
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