Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287056
Anouar Kherchouche, Sid Ahmed Fezza, W. Hamidouche, O. Déforges
The deep neural networks (DNNs) have been adopted in a wide spectrum of applications. However, it has been demonstrated that their are vulnerable to adversarial examples (AEs): carefully-crafted perturbations added to a clean input image. These AEs fool the DNNs which classify them incorrectly. Therefore, it is imperative to develop a detection method of AEs allowing the defense of DNNs. In this paper, we propose to characterize the adversarial perturbations through the use of natural scene statistics. We demonstrate that these statistical properties are altered by the presence of adversarial perturbations. Based on this finding, we design a classifier that exploits these scene statistics to determine if an input is adversarial or not. The proposed method has been evaluated against four prominent adversarial attacks and on three standards datasets. The experimental results have shown that the proposed detection method achieves a high detection accuracy, even against strong attacks, while providing a low false positive rate.
{"title":"Natural Scene Statistics for Detecting Adversarial Examples in Deep Neural Networks","authors":"Anouar Kherchouche, Sid Ahmed Fezza, W. Hamidouche, O. Déforges","doi":"10.1109/MMSP48831.2020.9287056","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287056","url":null,"abstract":"The deep neural networks (DNNs) have been adopted in a wide spectrum of applications. However, it has been demonstrated that their are vulnerable to adversarial examples (AEs): carefully-crafted perturbations added to a clean input image. These AEs fool the DNNs which classify them incorrectly. Therefore, it is imperative to develop a detection method of AEs allowing the defense of DNNs. In this paper, we propose to characterize the adversarial perturbations through the use of natural scene statistics. We demonstrate that these statistical properties are altered by the presence of adversarial perturbations. Based on this finding, we design a classifier that exploits these scene statistics to determine if an input is adversarial or not. The proposed method has been evaluated against four prominent adversarial attacks and on three standards datasets. The experimental results have shown that the proposed detection method achieves a high detection accuracy, even against strong attacks, while providing a low false positive rate.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"259 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120939650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287133
A. Wazir, H. A. Karim, Mohd Haris Lye Abdullah, Sarina Mansor, Nouar Aldahoul, M. F. A. Fauzi, John See
Excessive content of profanity in audio and video files has proven to shape one’s character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship through automated and robust detection by deep convolutional neural networks (CNNs). A dataset of foul language was collected and processed for the computation of audio spectrogram images that serve as an input to evaluate the classification of foul language. The proposed model was first tested for 2-class (Foul vs Normal) classification problem, the foul class is then further decomposed into a 10-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of curse words classification with 1.24-2.71 Error Rate (ER) for 2-class and 5.49-8.30 F1- score. Proposed Resnet50 architecture outperforms other models in terms of accuracy, sensitivity, specificity, F1-score.
{"title":"Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN","authors":"A. Wazir, H. A. Karim, Mohd Haris Lye Abdullah, Sarina Mansor, Nouar Aldahoul, M. F. A. Fauzi, John See","doi":"10.1109/MMSP48831.2020.9287133","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287133","url":null,"abstract":"Excessive content of profanity in audio and video files has proven to shape one’s character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship through automated and robust detection by deep convolutional neural networks (CNNs). A dataset of foul language was collected and processed for the computation of audio spectrogram images that serve as an input to evaluate the classification of foul language. The proposed model was first tested for 2-class (Foul vs Normal) classification problem, the foul class is then further decomposed into a 10-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of curse words classification with 1.24-2.71 Error Rate (ER) for 2-class and 5.49-8.30 F1- score. Proposed Resnet50 architecture outperforms other models in terms of accuracy, sensitivity, specificity, F1-score.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116347502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287067
H. Huang, I. Schiopu, A. Munteanu
The paper proposes a novel low-complexity Convolutional Neural Network (CNN) architecture for block-wise angular intra-prediction in lossless video coding. The proposed CNN architecture is designed based on an efficient patch processing layer structure. The proposed CNN-based prediction method is employed to process an input patch containing the causal neighborhood of the current block in order to directly generate the predicted block. The trained models are integrated in the HEVC video coding standard to perform CNN-based angular intra-prediction and to compete with the conventional HEVC prediction. The proposed CNN architecture contains a reduced number of parameters equivalent to only 37% of that of the state-of-the-art reference CNN architecture. Experimental results show that the inference runtime is also reduced by around 5.5% compared to that of the reference method. At the same time, the proposed coding systems yield 83% to 91% of the compression performance of the reference method. The results demonstrate the potential of structural and complexity optimizations in CNN-based intra-prediction for lossless HEVC.
{"title":"Low-Complexity Angular Intra-Prediction Convolutional Neural Network for Lossless HEVC","authors":"H. Huang, I. Schiopu, A. Munteanu","doi":"10.1109/MMSP48831.2020.9287067","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287067","url":null,"abstract":"The paper proposes a novel low-complexity Convolutional Neural Network (CNN) architecture for block-wise angular intra-prediction in lossless video coding. The proposed CNN architecture is designed based on an efficient patch processing layer structure. The proposed CNN-based prediction method is employed to process an input patch containing the causal neighborhood of the current block in order to directly generate the predicted block. The trained models are integrated in the HEVC video coding standard to perform CNN-based angular intra-prediction and to compete with the conventional HEVC prediction. The proposed CNN architecture contains a reduced number of parameters equivalent to only 37% of that of the state-of-the-art reference CNN architecture. Experimental results show that the inference runtime is also reduced by around 5.5% compared to that of the reference method. At the same time, the proposed coding systems yield 83% to 91% of the compression performance of the reference method. The results demonstrate the potential of structural and complexity optimizations in CNN-based intra-prediction for lossless HEVC.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115961842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287075
Yabing Cui, Yuanzhi Yao, Nenghai Yu
As a newly added in-loop filtering technique in High Efficiency Video Coding (HEVC), sample adaptive offset (SAO) can be utilized to embed messages for video steganography. This paper presents a novel SAO-based HEVC video steganographic scheme. The main principle is to design a suitable distortion function which expresses the embedding impacts on offsets based on minimizing embedding distortion. Two factors including the sample rate-distortion cost fluctuation and the sample statistical characteristic are considered in embedding distortion definition. Adaptive message embedding is implemented using syndrome-trellis codes (STC). Experimental results demonstrate the merits of the proposed scheme in terms of undetectability and video coding performance.
{"title":"Defining Embedding Distortion for Sample Adaptive Offset-Based HEVC Video Steganography","authors":"Yabing Cui, Yuanzhi Yao, Nenghai Yu","doi":"10.1109/MMSP48831.2020.9287075","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287075","url":null,"abstract":"As a newly added in-loop filtering technique in High Efficiency Video Coding (HEVC), sample adaptive offset (SAO) can be utilized to embed messages for video steganography. This paper presents a novel SAO-based HEVC video steganographic scheme. The main principle is to design a suitable distortion function which expresses the embedding impacts on offsets based on minimizing embedding distortion. Two factors including the sample rate-distortion cost fluctuation and the sample statistical characteristic are considered in embedding distortion definition. Adaptive message embedding is implemented using syndrome-trellis codes (STC). Experimental results demonstrate the merits of the proposed scheme in terms of undetectability and video coding performance.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124021560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287128
A. Pérez-López, A. Politis, E. Gómez
Reverberation time is an important room acoustic parameter, useful for many acoustic signal processing applications. Most of the existing work on blind reverberation time estimation focuses on the single-channel case. However, the recent developments and interest on immersive audio have brought to the market a number of spherical microphone arrays, together with the usage of ambisonics as a standard spatial audio convention. This work presents a novel blind reverberation time estimation method, which specifically targets ambisonic recordings, a field that remained unexplored to the best of our knowledge. Experimental validation on a synthetic reverberant dataset shows that the proposed algorithm outperforms state-of-the-art methods under most evaluation criteria in low noise conditions.
{"title":"Blind reverberation time estimation from ambisonic recordings","authors":"A. Pérez-López, A. Politis, E. Gómez","doi":"10.1109/MMSP48831.2020.9287128","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287128","url":null,"abstract":"Reverberation time is an important room acoustic parameter, useful for many acoustic signal processing applications. Most of the existing work on blind reverberation time estimation focuses on the single-channel case. However, the recent developments and interest on immersive audio have brought to the market a number of spherical microphone arrays, together with the usage of ambisonics as a standard spatial audio convention. This work presents a novel blind reverberation time estimation method, which specifically targets ambisonic recordings, a field that remained unexplored to the best of our knowledge. Experimental validation on a synthetic reverberant dataset shows that the proposed algorithm outperforms state-of-the-art methods under most evaluation criteria in low noise conditions.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287058
Xiaoyue Jiang, Ding Wang, D. Tran, S. Kiranyaz, M. Gabbouj, Xiaoyi Feng
Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same physical characteristic. This brings great challenges for material recognition. Besides suitable features, a powerful classifier also can improve the overall recognition performance. Due to the limitations of classical linear neurons, used in all shallow and deep neural networks, such as CNN, we propose to apply the generalized operational neurons to construct a classifier adaptively. These generalized operational perceptrons (GOP) contain a set of linear and nonlinear neurons, and possess a structure that can be built progressively. This makes GOP classifier more compact and can easily discriminate complex classes. The experiments demonstrate that GOP networks trained on a small portion of the data (4%) can achieve comparable performances to state-of-the-arts models trained on much larger portions of the dataset.
{"title":"Generalized Operational Classifiers for Material Identification","authors":"Xiaoyue Jiang, Ding Wang, D. Tran, S. Kiranyaz, M. Gabbouj, Xiaoyi Feng","doi":"10.1109/MMSP48831.2020.9287058","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287058","url":null,"abstract":"Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same physical characteristic. This brings great challenges for material recognition. Besides suitable features, a powerful classifier also can improve the overall recognition performance. Due to the limitations of classical linear neurons, used in all shallow and deep neural networks, such as CNN, we propose to apply the generalized operational neurons to construct a classifier adaptively. These generalized operational perceptrons (GOP) contain a set of linear and nonlinear neurons, and possess a structure that can be built progressively. This makes GOP classifier more compact and can easily discriminate complex classes. The experiments demonstrate that GOP networks trained on a small portion of the data (4%) can achieve comparable performances to state-of-the-arts models trained on much larger portions of the dataset.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133798696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/mmsp48831.2020.9287137
{"title":"MMSP 2020 Index","authors":"","doi":"10.1109/mmsp48831.2020.9287137","DOIUrl":"https://doi.org/10.1109/mmsp48831.2020.9287137","url":null,"abstract":"","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132595380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287159
Nina Žižakić, A. Pižurica
In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and then learns to improve upon it in an unsupervised manner. We demonstrate the use of this knowledge-transfer framework by constructing the learned BRIEF descriptor based on the well-known hand-crafted descriptor BRIEF. We implement our learned BRIEF with a convolutional autoencoder architecture. Evaluation on the HPatches benchmark for local image descriptors shows the effectiveness of the proposed approach in the tasks of patch retrieval, patch verification, and image matching.
{"title":"Learned BRIEF – transferring the knowledge from hand-crafted to learning-based descriptors","authors":"Nina Žižakić, A. Pižurica","doi":"10.1109/MMSP48831.2020.9287159","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287159","url":null,"abstract":"In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and then learns to improve upon it in an unsupervised manner. We demonstrate the use of this knowledge-transfer framework by constructing the learned BRIEF descriptor based on the well-known hand-crafted descriptor BRIEF. We implement our learned BRIEF with a convolutional autoencoder architecture. Evaluation on the HPatches benchmark for local image descriptors shows the effectiveness of the proposed approach in the tasks of patch retrieval, patch verification, and image matching.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114903195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-21DOI: 10.1109/MMSP48831.2020.9287122
Steve Goering, Robert Steger, Rakesh Rao Ramachandra Rao, A. Raake
Besides classical videos, videos of gaming matches, entire tournaments or individual sessions are streamed and viewed all over the world. The increased popularity of Twitch or YoutubeGaming shows the importance of additional research on gaming videos. One important pre-condition for live or offline encoding of gaming videos is the knowledge of game-specific properties. Knowing or automatically predicting the genre of a gaming video enables a more advanced and optimized encoding pipeline for streaming providers, especially because gaming videos of different genres vary a lot from classical 2D video, e.g., considering the CGI content, textures or camera motion. We describe several computer-vision based features that are optimized for speed and motivated by characteristics of popular games, to automatically predict the genre of a gaming video. Our prediction system uses random forest and gradient boosting trees as underlying machine-learning techniques, combined with feature selection. For the evaluation of our approach we use a dataset that was built as part of this work and consists of recorded gaming sessions for 6 genres from Twitch. In total 351 different videos are considered. We show that our prediction approach shows a good performance in terms of f1-score. Besides the evaluation of different machine-learning approaches, we additionally investigate the influence of the hyper-parameters for the algorithms.
{"title":"Automated Genre Classification for Gaming Videos","authors":"Steve Goering, Robert Steger, Rakesh Rao Ramachandra Rao, A. Raake","doi":"10.1109/MMSP48831.2020.9287122","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287122","url":null,"abstract":"Besides classical videos, videos of gaming matches, entire tournaments or individual sessions are streamed and viewed all over the world. The increased popularity of Twitch or YoutubeGaming shows the importance of additional research on gaming videos. One important pre-condition for live or offline encoding of gaming videos is the knowledge of game-specific properties. Knowing or automatically predicting the genre of a gaming video enables a more advanced and optimized encoding pipeline for streaming providers, especially because gaming videos of different genres vary a lot from classical 2D video, e.g., considering the CGI content, textures or camera motion. We describe several computer-vision based features that are optimized for speed and motivated by characteristics of popular games, to automatically predict the genre of a gaming video. Our prediction system uses random forest and gradient boosting trees as underlying machine-learning techniques, combined with feature selection. For the evaluation of our approach we use a dataset that was built as part of this work and consists of recorded gaming sessions for 6 genres from Twitch. In total 351 different videos are considered. We show that our prediction approach shows a good performance in terms of f1-score. Besides the evaluation of different machine-learning approaches, we additionally investigate the influence of the hyper-parameters for the algorithms.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115686579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work proposes a bi-directional intra prediction-based measurement coding algorithm for compressive sensing images. Compressive sensing is capable of reducing the size of the sparse signals, in which the high-dimensional signals are represented by the under-determined linear measurements. In order to explore the spatial redundancy in measurements, the corresponding pixel domain information extracted using the structure of measurement matrix. Firstly, the mono-directional prediction modes (i.e. horizontal mode and vertical mode), which refer to the nearest information of neighboring pixel blocks, are obtained by the structure of the measurement matrix. Secondly, we design bi-directional intra prediction modes (i.e. Diagonal + Horizontal, Diagonal + Vertical) base on the already obtained mono-directional prediction modes. Experimental results show that this work improves 0.01 - 0.02 dB PSNR improvement and the birate reductions of on average 19%, up to 36% compared to the state-of-the-art.
{"title":"Bi-directional intra prediction based measurement coding for compressive sensing images","authors":"Thuy Thi Thu Tran, Jirayu Peetakul, Chi Do-Kim Pham, Jinjia Zhou","doi":"10.1109/MMSP48831.2020.9287074","DOIUrl":"https://doi.org/10.1109/MMSP48831.2020.9287074","url":null,"abstract":"This work proposes a bi-directional intra prediction-based measurement coding algorithm for compressive sensing images. Compressive sensing is capable of reducing the size of the sparse signals, in which the high-dimensional signals are represented by the under-determined linear measurements. In order to explore the spatial redundancy in measurements, the corresponding pixel domain information extracted using the structure of measurement matrix. Firstly, the mono-directional prediction modes (i.e. horizontal mode and vertical mode), which refer to the nearest information of neighboring pixel blocks, are obtained by the structure of the measurement matrix. Secondly, we design bi-directional intra prediction modes (i.e. Diagonal + Horizontal, Diagonal + Vertical) base on the already obtained mono-directional prediction modes. Experimental results show that this work improves 0.01 - 0.02 dB PSNR improvement and the birate reductions of on average 19%, up to 36% compared to the state-of-the-art.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122180891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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