Special Issue "Digital Signal, Image and Video Processing for Emerging Multimedia Technology"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editor

Guest Editor
Prof. Dr. Byung-Gyu Kim

Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Korea
Website | E-Mail
Interests: image processing; pattern recognition; computer vision; signal, image and video processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The technologies underlying the development of these recognition and emerging services are based on essential signal and image processing algorithms. In addition, the recent realistic media services, mixed reality, augmented reality and virtual reality media services also require very high-definition media creation, personalization, and transmission technologies, and this demand continues to grow. To accommodate these needs, international standardization and industry are studying various digital signal and image processing technologies to provide a variety of new or future media services.While this Special Issue invites topics broadly across the advanced signal, image and video processing algorithms and technologies for emerging multimedia services, some specific topics include, but are not limited to: 

- Signal/image/video processing algorithm for advanced machine learning

- Fast and complexity-reducing mechanisms to support real-time systems

- Protecting technologies for privacy/personalized information

- Advanced circuit and system design and implementation for emerging multimedia services

- Image/video-based recognition algorithms using deep neural networks

- Novel applications for emerging multimedia services

- Efficient media sharing schemes in distributed environments 

Prof. Dr. Byung-Gyu Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • Emerging multimedia
  • Signal/image/video processing
  • Real-time systems
  • Advanced machine learning
  • Image/video-based deep learning

Published Papers (6 papers)

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Research

Open AccessArticle
Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation
Electronics 2019, 8(7), 753; https://doi.org/10.3390/electronics8070753
Received: 14 May 2019 / Revised: 28 June 2019 / Accepted: 29 June 2019 / Published: 3 July 2019
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Abstract
In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampling ratio as well [...] Read more.
In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampling ratio as well as adopting error analysis in the process of reconstruction. The weighted mean information entropy is adopted as the basis for partitioning of BCS which results in a flexible block group. Furthermore, the synthetic feature (SF) based on local saliency and variance is introduced to step-less adaptive sampling that works well in distinguishing and sampling between smooth blocks and detail blocks. The error analysis method is used to estimate the optimal number of iterations in sparse reconstruction. Based on the above points, an altered adaptive block-compressive sensing algorithm with flexible partitioning and error analysis is proposed in the article. On the one hand, it provides a feasible solution for the partitioning and sampling of an image, on the other hand, it also changes the iteration stop condition of reconstruction, and then improves the quality of the reconstructed image. The experimental results verify the effectiveness of the proposed algorithm and illustrate a good improvement in the indexes of the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Gradient Magnitude Similarity Deviation (GMSD), and Block Effect Index (BEI). Full article
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Open AccessArticle
Wiener–Granger Causality Theory Supported by a Genetic Algorithm to Characterize Natural Scenery
Electronics 2019, 8(7), 726; https://doi.org/10.3390/electronics8070726
Received: 9 May 2019 / Revised: 17 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
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Abstract
Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image [...] Read more.
Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image Retrieval (CBIR) methodology (to obtain different texture features); later, a Genetic Algorithm (GA) is implemented to select the most relevant natural elements from the images which share similar causality patterns. The proposed method is comprised of a sequential feature extraction stage, a time series conformation task, a causality estimation phase, causality feature selection throughout the GA implementation (using the classification process into the fitness function). A classification stage was implemented and 700 images of natural scenery were used for validating the results. Tested in the distribution system implementation, the technical efficiency of the developed system is 100% and 96% for resubstitution and cross-validation methodologies, respectively. This proposal could help with recognizing natural scenarios in the navigation of an autonomous car or possibly a drone, being an important element in the safety of autonomous vehicles navigation. Full article
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Open AccessArticle
An Efficient Separable Reversible Data Hiding Using Paillier Cryptosystem for Preserving Privacy in Cloud Domain
Electronics 2019, 8(6), 682; https://doi.org/10.3390/electronics8060682
Received: 23 April 2019 / Revised: 31 May 2019 / Accepted: 3 June 2019 / Published: 17 June 2019
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Abstract
Reversible data hiding in encrypted image (RDHEI) is advantageous to scenarios where complete recovery of the original cover image and additional data are required. In some of the existing RDHEI schemes, the image pre-processing step involved is an overhead for the resource-constrained devices [...] Read more.
Reversible data hiding in encrypted image (RDHEI) is advantageous to scenarios where complete recovery of the original cover image and additional data are required. In some of the existing RDHEI schemes, the image pre-processing step involved is an overhead for the resource-constrained devices on the sender’s side. In this paper, an efficient separable reversible data hiding scheme over a homomorphically encrypted image that assures privacy preservation of the contents in the cloud environment is proposed. This proposed scheme comprises three stakeholders: content-owner, data hider, and receiver. Initially, the content-owner encrypts the original image and sends the encrypted image to the data hider. The data hider embeds the encrypted additional data into the encrypted image and then sends the marked encrypted image to the receiver. On the receiver’s side, both additional data and the original image are extracted in a separable manner, i.e., additional data and the original image are extracted independently and completely from the marked encrypted image. The present scheme uses public key cryptography and facilitates the encryption of the original image on the content-owner side, without any pre-processing step involved. In addition, our experiment used distinct images to demonstrate the image-independency and the obtained results show high embedding rate where the peak signal noise ratio (PSNR) is +∞ dB for the directly decrypted image. Finally, a comparison is drawn, which shows that the proposed scheme is an optimized approach for resource-constrained devices as it omits the image pre-processing step. Full article
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Open AccessArticle
Wavelet-Integrated Deep Networks for Single Image Super-Resolution
Electronics 2019, 8(5), 553; https://doi.org/10.3390/electronics8050553
Received: 25 April 2019 / Revised: 12 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
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Abstract
We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined [...] Read more.
We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Full article
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Open AccessArticle
Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth
Electronics 2019, 8(5), 514; https://doi.org/10.3390/electronics8050514
Received: 5 April 2019 / Revised: 22 April 2019 / Accepted: 22 April 2019 / Published: 9 May 2019
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Abstract
With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using [...] Read more.
With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity. Full article
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Open AccessArticle
CCTV Video Processing Metadata Security Scheme Using Character Order Preserving-Transformation in the Emerging Multimedia
Electronics 2019, 8(4), 412; https://doi.org/10.3390/electronics8040412
Received: 14 February 2019 / Revised: 13 March 2019 / Accepted: 18 March 2019 / Published: 9 April 2019
Cited by 2 | PDF Full-text (3061 KB) | HTML Full-text | XML Full-text
Abstract
Intelligent video surveillance environments enable the gathering of various types of information about the object being recorded, through the analysis of real-time video data collected from CCTV systems and the automated processing that utilize the information. However, the surveillance environments face the risks [...] Read more.
Intelligent video surveillance environments enable the gathering of various types of information about the object being recorded, through the analysis of real-time video data collected from CCTV systems and the automated processing that utilize the information. However, the surveillance environments face the risks of privacy exposure, which necessitates secure countermeasures. Video meta-data, in particular, contain various types of personal information that is analyzed based on big data and are thus fraught with high levels of confidentiality breaches. Despite such risks, it is not appropriate to implement encryption for video meta-data considering the efficiency issue. This paper proposes a character order preserving (COP)-transformation technique that allows the secure protection of video meta-data. The proposed technique has the merits of preventing the recovery of original meta information through meta transformation and allowing direct queries on the data transformed, increasing significantly both security and efficiency in the video meta-data processing. Full article
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