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Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 9510

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Special Issue Editors

Dr. Ionut Schiopu

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Tampere Handset Camera Innovation Lab, Huawei Technologies Oy (Finland) Co., Ltd., 33720 Tampere, Finland
Interests: image and video coding; lossless data compression; light field image compression; event data compression; point cloud compression; deep-learning-based quality enhancement of coding artefact removal; deep-learning-based depth estimation; deep-learning-based semantic segmentation; deep-learning-based instance segmentation; deep-learning-based image/video deblurring; hybrid coding scheme; learning-based image coding

Dr. Radu Ciprian Bilcu

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Guest Editor

Tampere Handset Camera Innovation Lab, Huawei Technologies Oy (Finland) Co., Ltd., 33720 Tampere, Finland
Interests: image signal processing; computer vision; signal processing for event cameras; event camera application for computer vision; 3D sensing and applications; LiDAR; ToF camera

Special Issue Information

Dear Colleagues,

The main goal of this Special Issue is to offer young researchers an opportunity to publish on an open access platform their most recent research breakthroughs in the field of computer science and engineering.

This Special Issue aims to gather novel approaches from various research fields in image/video coding and processing and computer vision. Topics of interest include but are not limited to:

Image and video coding;
Light field image compression;
Event (spike) data compression;
Point cloud compression;
Deep-learning-based quality enhancement of coding artefact removal;
Deep-learning-based depth estimation;
Deep-learning-based semantic segmentation;
Deep-learning-based instance segmentation;
Deep-learning-based image/video deblurring;
Learning-based image coding;
Image signal processing;
Event camera applications for computer vision;
3D sensing and applications;
LiDAR;
ToF camera.

Dr. Ionut Schiopu
Dr. Radu Ciprian Bilcu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

image and video coding
visual data compression
deep learning for image/video processing and computer vision
image signal processing
3D sensing and applications

Published Papers (10 papers)

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Research

21 pages, 1120 KiB

Open AccessArticle

Several Approaches for the Prediction of the Operating Modes of a Wind Turbine

by Hannah Yun, Ciprian Doru Giurcăneanu and Gillian Dobbie

Electronics 2024, 13(8), 1504; https://doi.org/10.3390/electronics13081504 - 15 Apr 2024

Viewed by 390

Abstract

Growing concern about climate change has intensified efforts to use renewable energy, with wind energy highlighted as a growing source. It is known that wind turbines are characterized by distinct operating modes that reflect production efficiency. In this work, we focus on the forecasting problem for univariate discrete-valued time series of operating modes. We define three prediction strategies to overcome the difficulties associated with missing data. These strategies are evaluated through experiments using five forecasting methods across two real-life datasets. Two of the forecasting methods have been introduced in the statistical literature as extensions of the well-known context algorithm: variable length Markov chains and Bayesian context tree. Additionally, we consider a Bayesian method based on conditional tensor factorization and two different smoothers from the classical tools for time series forecasting. After evaluating each pair prediction strategy/forecasting method in terms of prediction accuracy versus computational complexity, we provide guidance on the methods that are suitable for forecasting the time series of operating modes. The prediction results that we report demonstrate that high accuracy can be achieved with reduced computational resources. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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18 pages, 4394 KiB

Open AccessArticle

Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

by Fengqin Wang, Zhiying Wang and Qiuwen Zhang

Electronics 2023, 12(18), 3914; https://doi.org/10.3390/electronics12183914 - 17 Sep 2023

Viewed by 862

Abstract

The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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18 pages, 2611 KiB

Open AccessArticle

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

by Yanjun Wang, Yong Liu, Jinchao Zhao and Qiuwen Zhang

Electronics 2023, 12(15), 3314; https://doi.org/10.3390/electronics12153314 - 02 Aug 2023

Cited by 1 | Viewed by 1044

Abstract

The rapid advancement of information technology, particularly in artificial intelligence and communication, is driving significant transformations in video coding. There is a steadily increasing demand for high-definition video in society. The latest video coding standard, versatile video coding (VVC), offers significant improvements in coding efficiency compared with its predecessor, high-efficiency video coding (HEVC). The improvement in coding efficiency is achieved through the introduction of a quadtree with nested multi-type tree (QTMT). However, this increase in coding efficiency also leads to a rise in coding complexity. In an effort to decrease the computational complexity of VVC coding, our proposed algorithm utilizes a decision tree (DT)-based approach for coding unit (CU) partitioning. The algorithm uses texture features and decision trees to efficiently determine CU partitioning. The algorithm can be summarized as follows: firstly, a statistical analysis of the new features of the VVC is carried out. More representative features are considered to extract to train classifiers that match the framework. Secondly, we have developed a novel framework for rapid CU decision making that is specifically designed to accommodate the distinctive characteristics of QTMT partitioning. The framework predicts in advance whether the CU needs to be partitioned and whether QT partitioning is required. The framework improves the efficiency of the decision-making process by transforming the partition decision of QTMT into multiple binary classification problems. Based on the experimental results, it can be concluded that our method significantly reduces the coding time by 55.19%, whereas BDBR increases it by only 1.64%. These findings demonstrate that our method is able to maintain efficient coding performance while significantly saving coding time. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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13 pages, 2350 KiB

Open AccessArticle

Fast Algorithm for CU Size Decision Based on Ensemble Clustering for Intra Coding of VVC 3D Video Depth Map

by Wenjun Song, Guanxin Li and Qiuwen Zhang

Electronics 2023, 12(14), 3098; https://doi.org/10.3390/electronics12143098 - 17 Jul 2023

Viewed by 743

Abstract

As many new coding techniques and coding structures have been introduced to further improve the coding efficiency of depth maps in 3D video extensions, the coding complexity has been greatly increased. Fast algorithms are now needed to improve coding unit (CU) depth decisions as well as the coding pattern decision based on the coding. This paper presents an innovative machine learning-based approach aimed at mitigating the complexity associated with in-frame coding algorithms. We build different clustering models for different CU sizes to cluster CUs of the same size to decide their CU sizes. This is achieved by augmenting ensemble clustering through the expedited propagation of clustering similarities, considering CU with the same or similar texture complexity the same as for CU depth selection, which is informed by a comprehensive analysis of the original texture and its neighboring elements. The experimental findings demonstrate that the proposed scheme yields a substantial average reduction of 44.24% in the coding time. Remarkably, the corresponding Bjøntegaard delta bit rate (BDBR) increment observed for the synthetic view is a mere 0.26%. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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18 pages, 3230 KiB

Open AccessArticle

Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

by Hongchan Li, Peng Zhang, Baohua Jin and Qiuwen Zhang

Electronics 2023, 12(14), 3053; https://doi.org/10.3390/electronics12143053 - 12 Jul 2023

Cited by 1 | Viewed by 940

Abstract

Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) classifier to reduce the coding complexity of VVC and save more coding time. We extract spatial feature vectors based on the DenseNet network model. Spatial feature vectors are constructed by predicting the boundary probabilities of 4 × 4 blocks in 64 × 64 coding units. Then, using the spatial features as the input of the DT classifier, through the classification function of the DT classifier model, the top N division modes with higher prediction probability are selected, and other division modes are skipped to reduce the computational complexity. Finally, the optimal partition mode is selected by comparing the RD cost. Our proposed algorithm achieves 47.6% encoding time savings on VTM10.0, while BDBR only increases by 0.91%. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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18 pages, 1153 KiB

Open AccessArticle

On the Application of the Stability Methods to Time Series Data

by Vicky Deng and Ciprian Doru Giurcăneanu

Electronics 2023, 12(13), 2988; https://doi.org/10.3390/electronics12132988 - 07 Jul 2023

Viewed by 726

Abstract

The important problem of selecting the predictors in a high-dimensional case where the number of candidates is larger than the sample size is often solved by the researchers from the signal processing community using the orthogonal matching pursuit algorithm or other greedy algorithms. In this work, we show how the same problem can be solved by applying methods based on the concept of stability. Even if it is not a new concept, the stability is less known in the signal processing community. We illustrate the use of stability by presenting a relatively new algorithm from this family. As part of this presentation, we conduct a simulation study to investigate the effect of various parameters on the performance of the algorithm. Additionally, we compare the stability-based method with more than eighty variants of five different greedy algorithms in an experiment with air pollution data. The comparison demonstrates that the use of stability leads to promising results in the high-dimensional case. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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18 pages, 2954 KiB

Open AccessArticle

Low-Complexity Fast CU Classification Decision Method Based on LGBM Classifier

by Yanjun Wang, Yong Liu, Jinchao Zhao and Qiuwen Zhang

Electronics 2023, 12(11), 2488; https://doi.org/10.3390/electronics12112488 - 31 May 2023

Viewed by 955

Abstract

At present, the latest video coding standard is Versatile Video Coding (VVC). Although the coding efficiency of VVC is significantly improved compared to the previous generation, standard High-Efficiency Video Coding (HEVC), it also leads to a sharp increase in coding complexity. VVC significantly improves HEVC by adopting the quadtree with nested multi-type tree (QTMT) partition structure, which has been proven to be very effective. This paper proposes a low-complexity fast coding unit (CU) partition decision method based on the light gradient boosting machine (LGBM) classifier. Representative features were extracted to train a classifier matching the framework. Secondly, a new fast CU decision framework was designed for the new features of VVC, which could predict in advance whether the CU was divided, whether it was divided by quadtree (QT), and whether it was divided horizontally or vertically. To solve the multi-classification problem, the technique of creating multiple binary classification problems was used. Subsequently, a multi-threshold decision-making scheme consisting of four threshold points was proposed, which achieved a good balance between time savings and coding efficiency. According to the experimental results, our method achieved a significant reduction in encoding time, ranging from 47.93% to 54.27%, but only improved the Bjøntegaard delta bit-rate (BDBR) by 1.07%~1.57%. Our method showed good performance in terms of both encoding time reduction and efficiency. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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26 pages, 5025 KiB

Open AccessFeature PaperArticle

Memory-Efficient Fixed-Length Representation of Synchronous Event Frames for Very-Low-Power Chip Integration

by Ionut Schiopu and Radu Ciprian Bilcu

Electronics 2023, 12(10), 2302; https://doi.org/10.3390/electronics12102302 - 19 May 2023

Cited by 2 | Viewed by 974

Abstract

The new event cameras are now widely used in many computer vision applications. Their high raw data bitrate levels require a more efficient fixed-length representation for low-bandwidth transmission from the event sensor to the processing chip. A novel low-complexity lossless compression framework is proposed for encoding the synchronous event frames (EFs) by introducing a novel memory-efficient fixed-length representation suitable for hardware implementation in the very-low-power (VLP) event-processing chip. A first contribution proposes an improved representation of the ternary frames using pixel-group frame partitioning and symbol remapping. Another contribution proposes a novel low-complexity memory-efficient fixed-length representation using multi-level lookup tables (LUTs). Complex experimental analysis is performed using a set of group-size configurations. For very-large group-size configurations, an improved representation is proposed using a mask-LUT structure. The experimental evaluation on a public dataset demonstrates that the proposed fixed-length coding framework provides at least two times the compression ratio relative to the raw EF representation and a close performance compared with variable-length video coding standards and variable-length state-of-the-art image codecs for lossless compression of ternary EFs generated at frequencies bellow one KHz. To our knowledge, the paper is the first to introduce a low-complexity memory-efficient fixed-length representation for lossless compression of synchronous EFs, suitable for integration into a VLP event-processing chip. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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17 pages, 1720 KiB

Open AccessArticle

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

by Chaoqin Zhang, Wentao Yang and Qiuwen Zhang

Electronics 2023, 12(9), 1967; https://doi.org/10.3390/electronics12091967 - 23 Apr 2023

Cited by 3 | Viewed by 1057

Abstract

In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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13 pages, 2167 KiB

Open AccessArticle

A Fast VVC Intra Prediction Based on Gradient Analysis and Multi-Feature Fusion CNN

by Zhiyong Jing, Wendi Zhu and Qiuwen Zhang

Electronics 2023, 12(9), 1963; https://doi.org/10.3390/electronics12091963 - 23 Apr 2023

Cited by 2 | Viewed by 1141

Abstract

The Joint Video Exploration Team (JVET) has created the Versatile Video Coding Standard (VVC/H.266), the most up-to-date video coding standard, offering a broad selection of coding tools. The maturity of commercial VVC codecs can significantly reduce costs and improve coding efficiency. However, the latest video coding standards have introduced binomial and trinomial tree partitioning methods, which cause the coding units (CUs) to have various shapes, increasing the complexity of coding. This article proposes a technique to simplify VVC intra prediction through the use of gradient analysis and a multi-feature fusion CNN. The gradient of CUs is computed by employing the Sobel operator, the calculation results are used for predecision-making. Further decisions can be made by CNN for coding units that cannot be judged whether they should be segmented or not. We calculate the standard deviation (SD) and the initial depth as the input features of the CNN. To implement this method, the initial depth can be determined by constructing a segmented depth prediction dictionary. For the initial segmentation depth of the coding unit, regardless of its shape, it can also be determined by consulting the dictionary. The algorithm can determine whether to split CUs of varying sizes, decreasing the complexity of the CU division process and making VVC more practical. Experimental results demonstrate that the proposed algorithm can reduce encoding time by 36.56% with a minimal increase of 1.06% Bjøntegaard delta bit rate (BD-BR) compared to the original algorithm. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing)

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