Lossless Steganography on Orthogonal Vector for 3D H.264 with Limited Distortion Diffusion

2021 ◽  
Author(s):  
Juan Zhao ◽  
Zhitang Li
Keyword(s):  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
Experimental Results ◽  
Signal To Noise ◽  
Cosine Transform ◽  
Orthogonal Vector ◽  
Leibler Divergence ◽  
Transform Coefficients ◽  
Distortion Drift ◽  
Limited Distortion

In order to improve the undetectability, a lossless algorithm based on orthogonal vectors with limited distortion diffusion for 3D H.264 video is proposed in this paper. Inter-view distortion drift is avoided by embedding data into frames, which do not predict other views. Three conditions and pairs of coefficients are proposed to prevent intra-frame distortion diffusion. Several quantized discrete cosine transform coefficients are chosen from an embeddable luminance 4×4 block to construct a carrier vector, which is modified by an offset vector. When the carrier vector and the offset vector are orthogonal or near to be orthogonal, a data bit can be hidden. Experimental results indicate that the method is effective by enhancing peak signal-to-noise ratio with 7.5dB and reducing the Kullback-Leibler divergence with 0.07 at least. More than 1.7×1015 ways could be utilized for constructing the vectors, so it is more difficult for others to steal data.

scholarly journals A Unified Algorithm for Subband-Based Discrete Cosine Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-31 ◽  
Author(s):  
Lu-Ting Ko ◽  
Jwu-E Chen ◽  
Hsi-Chin Hsin ◽  
Yaw-Shih Shieh ◽  
Tze-Yun Sung
Keyword(s):  
Discrete Cosine Transform ◽  
System Performance ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Reconfigurable Architecture ◽  
Signal To Noise ◽  
Cosine Transform ◽  
Unified Algorithm ◽  
Inverse Dct ◽  
Single Processor

Discrete cosine transform (DCT) and inverse DCT (IDCT) have been widely used in many image processing systems and real-time computation of nonlinear time series. In this paper, the unified DCT/IDCT algorithm based on the subband decompositions of a signal is proposed. It is derived from the data flow of subband decompositions with factorized coefficient matrices in a recursive manner. The proposed algorithm only requires(4(log2n)−1−1)and(4(log2n)−1−1)/3multiplication time forn-point DCT and IDCT, with a single multiplier and a single processor, respectively. Moreover, the peak signal-to-noise ratio (PSNR) of the proposed algorithm outperforms the conventional DCT/IDCT. As a result, the subband-based approach to DCT/IDCT is preferable to the conventional approach in terms of computational complexity and system performance. The proposed reconfigurable architecture of linear array DCT/IDCT processor has been implemented by FPGA.

scholarly journals PERBAIKAN CITRA DENGAN NOISE MISSING BLOCK MENGGUNAKAN IMPLEMENTASI ALGORITMA PROJECTION ONTO CONVEX SETS (POCS)

KONVERGENSI ◽  
2016 ◽  
Vol 11 (01) ◽  
Author(s):  
Dwi Harini Sulistyawati ◽  
Heri Setyo Utomo
Keyword(s):  
Mean Square Error ◽  
Discrete Cosine Transform ◽  
Convex Sets ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Signal To Noise ◽  
Cosine Transform ◽  
Projection Onto Convex Sets ◽  
Noise Ratio

Ketajaman kualitas gambar sangat diperlukan untuk melihat dan mengamati gambar dengan jelas tanpa gangguan seperti blur atau noise. Dalam proses pengiriman atau penyimpanan, gambar dapat terganggu dalam bentuk kerusakan pada bagian-bagian tertentu yang hilang atau blok-blok piksel, kerusakan ini adalah bentuk utama dari kesalahan dalam suatu gambar. Jadi dengan mengimplementasikan algoritma rekonstruksi citra Projection Onto Convex Sets (POCS) pada domain Discrete Cosine Transform (DCT) untuk meningkatkan citra, terutama gambar yang mengalami kerusakan pada beberapa bagian yang hilang atau blok piksel. Untuk blok proses pemulihan ada beberapa langkah yang harus diambil termasuk deteksi garis, pembacaan jendela di sekitarnya dan vektor pemulihan. Dari hasil pengujian dapat disimpulkan bahwa metode ini dapat digunakan untuk pemulihan blok POCS dan nilai-nilai piksel yang sesuai dengan blok warna di sekitar blok. Pengukuran nilai kesalahan citra menggunakan Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR).

Speech Enhancement on Over-Complete Dictionary and Threshold Orthogonal Matching Pursuit

2014 ◽  
Vol 1044-1045 ◽  
pp. 1463-1468
Author(s):  
Xiao Cui ◽  
Wu Qing Zhang
Keyword(s):  
Discrete Cosine Transform ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Orthogonal Matching Pursuit ◽  
Cosine Transform ◽  
Perceptual Evaluation ◽  
Transform Coefficients

In order to suppress the noise, improve equipment's ability to further process information and improve the quality of voice, speech enhancement is often an important part of the speech signal preprocess. Contrastively analyze the characteristic that the clean speech signal coefficients in over-complete discrete cosine dictionary are much sparser than the traditional discrete cosine transform coefficients. Under noisy conditions, by setting the iterative threshold of orthogonal matching pursuit (OMP) algorithm, clean speech can be gotten, thus realize the speech enhancement. Simulation results of the signal waveform and spectrogram enhanced by the proposed algorithm are very similar to the original signal,comparative experiments also indicate that the signal to noise ratio (SNR) and the perceptual evaluation of speech quality (PESQ) score of the processed signal are superior to traditional discrete cosine transform (DCT).

scholarly journals Performance Analysis of DCT and WDCT Algorithms in Image Steganography

2021 ◽  
Author(s):  
Al Anood Najeeb ◽  
Somaya Al Maadeed ◽  
Noor Al Maadeed
Keyword(s):  
Deep Learning ◽  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
High Accuracy ◽  
Signal To Noise ◽  
Stego Image ◽  
Cosine Transform ◽  
Public Datasets ◽  
Frequency Domain Techniques ◽  
Made In

Frequency domain techniques such as Discrete Cosine Transform (DCT) and Warped Discrete Cosine Transform (WDCT) ensures high accuracy when compared with the spatial domain techniques. Therefore, these image steganographic methods were evaluated using public datasets to compare the performance of DCT and WDCT. After performing different tests using the datasets in each of the algorithms, a comparative analysis is made in terms of the Peak Signal-to-Noise Ratio (PSNR) metrics. The results indicate that the stego image generated after embedding the secret acquires high imperceptibility and robustness. The performance of the WDCT algorithm is higher as compared to the DCT algorithm and the resultant images produced are very less prone to noise attacks. In DCT and WDCT algorithms, the cover image will be split based on 8×8 pixel blocks and 2D DCT is applied on each pixel. The secret will be embedded inside DCT coefficient and inverse 2D DCT is applied to recover the secret. Therefore, these image steganographic techniques can be adopted to transfer the confidential messages in different sectors. In the future, other data hiding methods using deep learning could be implemented to increase the robustness and imperceptibility of covert messages.

scholarly journals Analisis Pengaruh Koefisien Watermarking pada Citra RGB dengan Menggunakan Perbandingan DCT dan DWT

2021 ◽  
Vol 7 (1) ◽  
pp. 62-70
Author(s):  
Kholidiyah Masykuroh
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
Rapid Development ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Cosine Transform ◽  
Noise Ratio ◽  
Rgb Image

Perkembangan internet yang semakin pesat dan kemudahan akses informasi digital menjadi peluang terjadinya cybercrime. Teknik pengamanan diperlukan pada informasi yang akan dikirimkan melalui media transmisi baik wireless maupun wired. Melalui penerapan watermarking diharapkan informasi terjaga dari serangan, penyisipan, penghapusan data, dan penggantian data. Penelitian ini membahas perbandingan metode watermarking menggunakan Discrete Cosine Transform (DCT) dan Discrete Wavelet Transform (DWT) pada citra berwarna. Means Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR) merupakan parameter uji untuk mengukur rasio perbandingan citra asli dan citra watermarking. Hasil pengujian menunjukkan bahwa perbedaan jenis transformasi yang digunakan mempengaruhi nilai SNR citra RGB. Citra RGB dengan menggunakan DCT memiliki nilai SNR yang lebih tinggi dibandingkan dengan DWT yang ditunjukkan dengan nilai SNR citra Peppers 30 dB. Nilai koefisien yang diberikan pada citra yang disisipkan bervariasi mulai dari 5, 10, dan 50. Nilai ini mempengaruhi nilai SNR citra RGB dengan transformasi DCT. Along with the rapid development of the internet and the ease of access to digital information, cybercrime has a chance to occur. Security techniques are needed for information to be transmitted via transmission media, either wireless or wired. The application of watermarking has an opportunity to protect the information from attacks, insertion, deletion, and data replacement. This research discusses the comparison of watermarking methods using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on color images. Means Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) are a method to test parameters for measure the original image's ratio to the watermark image. The test results showed that the different types of transformations affect the SNR value of the RGB image. RGB image using DCT has a higher SNR value than DWT, which is indicated by the SNR value of Peppers image 30 dB. The coefficient value given to the inserted image varies from 5, 10, and 50. This value affects the SNR value of RGB images with DCT transformation.

scholarly journals Penyembunyian Data pada File Video Menggunakan Metode LSB dan DCT

2014 ◽  
Vol 8 (1) ◽  
pp. 81
Author(s):  
Mahmuddin Yunus ◽  
Agus Harjoko
Keyword(s):  
Mean Square Error ◽  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Least Significant Bit ◽  
Combined Method ◽  
Signal To Noise ◽  
Video File ◽  
Cosine Transform ◽  
Noise Ratio

AbstrakPenyembunyian data pada file video dikenal dengan istilah steganografi video. Metode steganografi yang dikenal diantaranya metode Least Significant Bit (LSB) dan Discrete Cosine Transform (DCT). Dalam penelitian ini dilakukan penyembunyian data pada file video dengan menggunakan metode LSB, metode DCT, dan gabungan metode LSB-DCT. Sedangkan kualitas file video yang dihasilkan setelah penyisipan dihitung dengan menggunakan Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR).Uji eksperimen dilakukan berdasarkan ukuran file video, ukuran file berkas rahasia yang disisipkan, dan resolusi video.Hasil pengujian menunjukkan tingkat keberhasilan steganografi video dengan menggunakan metode LSB adalah 38%, metode DCT adalah 90%, dan gabungan metode LSB-DCT adalah 64%. Sedangkan hasil perhitungan MSE, nilai MSE metode DCT paling rendah dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan metode LSB-DCT mempunyai nilai yang lebih kecil dibandingkan metode LSB. Pada pengujian PSNR diperoleh databahwa nilai PSNR metode DCTlebih tinggi dibandingkan metode LSB dan gabungan metode LSB-DCT. Sedangkan nilai PSNR metode gabungan LSB-DCT lebih tinggi dibandingkan metode LSB.  Kata Kunci—Steganografi, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)                           AbstractHiding data in video files is known as video steganography. Some of the well known steganography methods areLeast Significant Bit (LSB) and Discrete Cosine Transform (DCT) method. In this research, data will be hidden on the video file with LSB method, DCT method, and the combined method of LSB-DCT. While the quality result of video file after insertion is calculated using the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experiments were conducted based on the size of the video file, the file size of the inserted secret files, and video resolution.The test results showed that the success rate of the video steganography using LSB method was 38%, DCT method was 90%, and the combined method of LSB-DCT was 64%. While the calculation of MSE, the MSE method DCT lower than the combined method of LSB and LSB-DCT method. While LSB-DCT method has asmaller value than the LSB method. The PNSR experiment showed that the DCT method PSNR value is higher than the combined method of LSB and LSB-DCT method. While PSNR combined method LSB-DCT higher compared LSB method.  Keywords—Steganography, Video, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)

scholarly journals IMPLEMENTASI STEGANOGRAPHY MENGGUNAKAN ALGORITMA DISCRETE COSINE TRANSFORM

2017 ◽  
Vol 2 (1) ◽  
pp. 35 ◽  
Author(s):  
Ahmad Adil Faruqi ◽  
Imam Fahrur Rozi
Keyword(s):  
Discrete Cosine Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Cosine Transform ◽  
Noise Ratio

Steganography  merupakan  metode  yang  berbeda  dengan  metode  kriptografi  yang  mengubah  sebuah informasi kedalam sebuah sandi sehingga tidak dapat dibaca tanpa mengetahui kunci atau sandi yang digunakan, namun keberadaannya tetap diketahui dan tidak disembunyikan. Sedangkan pada steganography informasi akan disembunyikan didalam suatu media pembawa sehingga tak seorangpun yang menyadari bahwa terdapat suatu informasi. Algoritma  yang  digunakan  dalam steganography  ini  adalah  algoritma  discrete  cosine  transform. Steganography menggunakan algoritma discrete cosine transform menghasilkan gambar steganography dengan memiliki kualitas yang tidak jauh berbeda dari gambar aslinya hal ini ditunjukkan dengan besarnya nilai rata-rata hasil peak signal to noise ratio (PSNR) dari tiga gambar yang berbeda yaitu sebesar 37.44 db. Hasil pengujian kompresi gambar steganography, menyimpulkan bahwa pesan dalam gambar hasil steganography menggunakan algoritma discrete cosine transform tahan terhadap kompresi gambar.

Discriminant DCT Feature Extraction

2006 ◽  
pp. 205-221
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Discrete Cosine Transform ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Experimental Results ◽  
Cosine Transform ◽  
Analysis Technique

This chapter provides a feature extraction approach that combines the discrete cosine transform (DCT) with LDA. The DCT-based frequency-domain analysis technique is introduced first. Then, we describe the presented discriminant DCT approach and analyze its theoretical properties. Finally, we offer detailed experimental results and a chapter summary.

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