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  • Open Access

    ARTICLE

    An Algorithm to Reduce Compression Ratio in Multimedia Applications

    Dur-e-Jabeen1,*, Tahmina Khan2, Rumaisa Iftikhar1, Ali Akbar Siddique1, Samiya Asghar1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 539-557, 2023, DOI:10.32604/cmc.2023.032393

    Abstract In recent years, it has been evident that internet is the most effective means of transmitting information in the form of documents, photographs, or videos around the world. The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual quality. During transmission, this massive data necessitates a lot of channel space. In order to overcome this problem, an effective visual compression approach is required to resize this large amount of data. This work is based on lossy image compression and is offered for static color images. The quantization procedure determines… More >

  • Open Access

    ARTICLE

    Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

    S. Rooban1,*, A. Yamini Naga Ratnam1, M. V. S. Ramprasad2, N. Subbulakshmi3, R. Uma Mageswari4

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5169-5184, 2022, DOI:10.32604/cmc.2022.027974

    Abstract Advanced technology used for arithmetic computing application, comprises greater number of approximate multipliers and approximate adders. Truncation and Rounding-based Scalable Approximate Multiplier (TRSAM) distinguish a variety of modes based on height (h) and truncation (t) as TRSAM (h, t) in the architecture. This TRSAM operation produces higher absolute error in Least Significant Bit (LSB) data shift unit. A new scalable approximate multiplier approach that uses truncation and rounding TRSAM (3, 7) is proposed to increase the multiplier accuracy. With the help of foremost one bit architecture, the proposed scalable approximate multiplier approach reduces the partial products. The proposed approximate TRSAM… More >

  • Open Access

    ARTICLE

    A Hybrid Security Framework for Medical Image Communication

    Walid El-Shafai1,2, Hayam A. Abd El-Hameed3, Ashraf A. M. Khalaf3, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2713-2730, 2022, DOI:10.32604/cmc.2022.028739

    Abstract Authentication of the digital image has much attention for the digital revolution. Digital image authentication can be verified with image watermarking and image encryption schemes. These schemes are widely used to protect images against forgery attacks, and they are useful for protecting copyright and rightful ownership. Depending on the desirable applications, several image encryption and watermarking schemes have been proposed to moderate this attention. This framework presents a new scheme that combines a Walsh Hadamard Transform (WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding (DRPE). First, on the sender side, the secret medical… More >

  • Open Access

    ARTICLE

    Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

    Samah Ibrahim Alshathri1,*, Desiree Juby Vincent2, V. S. Hari2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1371-1386, 2022, DOI:10.32604/cmc.2022.022458

    Abstract Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for denoising application using a combined… More >

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