HSV model based data hiding in video for watermark applications

2021 ◽  
pp. 1-12
Author(s):  
J. Jerisha Liby ◽  
T Jaya
Keyword(s):  
Data Hiding ◽  
Cross Correlation ◽  
Similarity Index ◽  
Visual Quality ◽  
Structural Similarity ◽  
Noise Filtering ◽  
Structural Similarity Index ◽  
Index Measurement ◽  
Embedding Rate ◽  
Better Than

This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R1, G1, B1), (R2, G2, B2) are converted to HSV (Hue, saturation, values) Coefficients (H1, S1, V1) and (H2, S2, V2). The ‘Value’ Coefficients V1 and V2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC).

scholarly journals Image Registration and Fusion using Moving Frame based Decomposition Framework Algorithm

2021 ◽  
Vol 10 (5) ◽  
pp. 57-63
Author(s):  
Pooja Aspalli ◽  
Prakash Pattan
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Structural Similarity ◽  
Moving Frame ◽  
Structural Similarity Index ◽  
Mapping Strategy ◽  
Index Measurement ◽  
Different Types ◽  
Algorithm Implementation

Image fusion is an important process in the medical image diagnostics methods. Fusing images by obtaining information from different source and different types of images(modals) called multi-modal image fusion. This paper implements an effective and fast spatial domain based multimodal image fusion using moving frame based decomposition (MFDF)method. Images from two different modalities are taken and decomposed to texture and approximation components. Weight mapping strategy is applied along with the guide filtering to fuse the approximation components using the final map. Weight mapping using the guide filtering is used for the fusing the images from different modalities. MATLAB is used for algorithm implementation. The results obtained are comparatively competitive with the recent publication[11]. Multi modal image fusion thus implemented gives promising results, when compared to moving frame decomposition framework method. The size and the blurring variable of the guiding filter is optimized to obtain a better Structural Similarity Index Measurement (SSIM).

scholarly journals Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

2019 ◽  
Vol 8 (6) ◽  
pp. 259 ◽  
Author(s):  
Debojit Biswas ◽  
Hongbo Su ◽  
Chengyi Wang ◽  
Aleksandar Stevanovic
Keyword(s):  
Moving Objects ◽  
Detection System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Transportation Systems ◽  
Structural Similarity Index ◽  
Current Frame ◽  
Index Measurement ◽  
Feature Based ◽  
Speed Accuracy

Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.

scholarly journals A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

2020 ◽  
Vol 12 (14) ◽  
pp. 2336 ◽  
Author(s):  
Shaobo Li ◽  
Jianhu Zhao ◽  
Hongmei Zhang ◽  
Zijun Bi ◽  
Siheng Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Low Rank ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Non Local ◽  
Better Than ◽  
Total Average

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

scholarly journals Inkonsistensi Antara Hasil Pengukuran SSIM Dengan Kondisi Visual Citra Hasil Metode Denoising Berbasis Ant Colony Optimization

Techno Com ◽  
2017 ◽  
Vol 16 (3) ◽  
pp. 315-324
Author(s):  
Septian Enggar Sukmana ◽  
Dewi Nurfitri Oktaviani
Keyword(s):  
Ant Colony Optimization ◽  
Similarity Index ◽  
Structural Similarity ◽  
Ant Colony ◽  
Structural Similarity Index ◽  
Index Measurement

Hasil denoising perlu dievaluasi untuk mengetahui nilai hasil secara terukur yang bertujuan untuk mendapatkan penilaian secara objektif. Seperti halnya teknik denoising, teknik evaluasi hasil citra juga dapat dilakukan baik pada domain spasial maupun domain frekuensi. Bagimanapun, hasil denoising pada citra sangat fokus pada bagian visual sehingga penilaian secara visual merupakan penilaian yang sangat penting. Penilaian secara visual dapat ditempuh melalui metode structural similarity index measurement (SSIM). SSIM melakukan teknik evaluasi dengan cara perbandingan antara citra hasil denoising dengan citra asli. Berdasarkan penelitian pada modifikasi metode denoising AntShrink telah ditemukan kelemahan SSIM terhadap kurangnya kemampuan SSIM dalam membaca kondisi blur. Untuk mengetahui ketepatan penemuan tersebut, evaluasi citra berbasis blur dan pemetaan SSIM dilakukan. Ketepatan temuan terbukti pada setiap tingkat noise yang tinggi (tingkat noise > 50 ) dengan representasi hasil citra yang memiliki banyak blur namun hasil penilaian indeks oleh SSIM dianggap tinggi. Hasil ini cukup serupa dengan hasil pada metode AntShrink yang menunjukkan trend angka lebih kecil daripada hasil metode usulan.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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