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

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

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

Article
Context-Based Inter Mode Decision Method for Fast Affine Prediction in Versatile Video Coding
Electronics 2021, 10(11), 1243; https://doi.org/10.3390/electronics10111243 - 24 May 2021
Viewed by 446
Abstract
Versatile Video Coding (VVC) is the most recent video coding standard developed by Joint Video Experts Team (JVET) that can achieve a bit-rate reduction of 50% with perceptually similar quality compared to the previous method, namely High Efficiency Video Coding (HEVC). Although VVC [...] Read more.
Versatile Video Coding (VVC) is the most recent video coding standard developed by Joint Video Experts Team (JVET) that can achieve a bit-rate reduction of 50% with perceptually similar quality compared to the previous method, namely High Efficiency Video Coding (HEVC). Although VVC can support the significant coding performance, it leads to the tremendous computational complexity of VVC encoder. In particular, VVC has newly adopted an affine motion estimation (AME) method to overcome the limitations of the translational motion model at the expense of higher encoding complexity. In this paper, we proposed a context-based inter mode decision method for fast affine prediction that determines whether the AME is performed or not in the process of rate-distortion (RD) optimization for optimal CU-mode decision. Experimental results showed that the proposed method significantly reduced the encoding complexity of AME up to 33% with unnoticeable coding loss compared to the VVC Test Model (VTM). Full article
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Article
Two-Dimensional Audio Compression Method Using Video Coding Schemes
Electronics 2021, 10(9), 1094; https://doi.org/10.3390/electronics10091094 - 06 May 2021
Viewed by 389
Abstract
As video compression is one of the core technologies that enables seamless media streaming within the available network bandwidth, it is crucial to employ media codecs to support powerful coding performance and higher visual quality. Versatile Video Coding (VVC) is the latest video [...] Read more.
As video compression is one of the core technologies that enables seamless media streaming within the available network bandwidth, it is crucial to employ media codecs to support powerful coding performance and higher visual quality. Versatile Video Coding (VVC) is the latest video coding standard developed by the Joint Video Experts Team (JVET) that can compress original data hundreds of times in the image or video; the latest audio coding standard, Unified Speech and Audio Coding (USAC), achieves a compression rate of about 20 times for audio or speech data. In this paper, we propose a pre-processing method to generate a two-dimensional (2D) audio signal as an input of a VVC encoder, and investigate the applicability to 2D audio compression using the video coding scheme. To evaluate the coding performance, we measure both signal-to-noise ratio (SNR) and bits per sample (bps). The experimental result shows the possibility of researching 2D audio encoding using video coding schemes. Full article
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Article
New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix
Electronics 2021, 10(9), 1066; https://doi.org/10.3390/electronics10091066 - 30 Apr 2021
Viewed by 396
Abstract
In the age of Information Technology, the day-life required transmitting millions of images between users. Securing these images is essential. Digital image encryption is a well-known technique used in securing image content. In image encryption techniques, digital images are converted into noise images [...] Read more.
In the age of Information Technology, the day-life required transmitting millions of images between users. Securing these images is essential. Digital image encryption is a well-known technique used in securing image content. In image encryption techniques, digital images are converted into noise images using secret keys, where restoring them to their originals required the same keys. Most image encryption techniques depend on two steps: confusion and diffusion. In this work, a new algorithm presented for image encryption using a hyperchaotic system and Fibonacci Q-matrix. The original image is confused in this algorithm, utilizing randomly generated numbers by the six-dimension hyperchaotic system. Then, the permutated image diffused using the Fibonacci Q-matrix. The proposed image encryption algorithm tested using noise and data cut attacks, histograms, keyspace, and sensitivity. Moreover, the proposed algorithm’s performance compared with several existing algorithms using entropy, correlation coefficients, and robustness against attack. The proposed algorithm achieved an excellent security level and outperformed the existing image encryption algorithms. Full article
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Article
WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
Electronics 2021, 10(8), 932; https://doi.org/10.3390/electronics10080932 - 14 Apr 2021
Viewed by 429
Abstract
Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be [...] Read more.
Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective. Full article
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Article
Cross-Modal Learning Based on Semantic Correlation and Multi-Task Learning for Text-Video Retrieval
Electronics 2020, 9(12), 2125; https://doi.org/10.3390/electronics9122125 - 11 Dec 2020
Viewed by 635
Abstract
Text-video retrieval tasks face a great challenge in the semantic gap between cross modal information. Some existing methods transform the text or video into the same subspace to measure their similarity. However, this kind of method does not consider adding a semantic consistency [...] Read more.
Text-video retrieval tasks face a great challenge in the semantic gap between cross modal information. Some existing methods transform the text or video into the same subspace to measure their similarity. However, this kind of method does not consider adding a semantic consistency constraint when associating the two modalities of semantic encoding, and the associated result is poor. In this paper, we propose a multi-modal retrieval algorithm based on semantic association and multi-task learning. Firstly, the multi-level features of video or text are extracted based on multiple deep learning networks, so that the information of the two modalities can be fully encoded. Then, in the public feature space where the two modalities information are mapped together, we propose a semantic similarity measurement and semantic consistency classification based on text-video features for a multi-task learning framework. With the semantic consistency classification task, the learning of semantic association task is restrained. So multi-task learning guides the better feature mapping of two modalities and optimizes the construction of unified feature subspace. Finally, the experimental results of our proposed algorithm on the Microsoft Video Description dataset (MSVD) and MSR-Video to Text (MSR-VTT) are better than the existing research, which prove that our algorithm can improve the performance of cross-modal retrieval. Full article
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Article
A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images
Electronics 2020, 9(9), 1500; https://doi.org/10.3390/electronics9091500 - 12 Sep 2020
Viewed by 772
Abstract
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. [...] Read more.
Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Full article
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