Jiang Zhu, Yan Zeng, Jianqi Li, Shujuan Tian, Haolin Liu
Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real-world sce-narios.
{"title":"An adaptive level set method based on joint estimation dealing with intensity inhomogeneity","authors":"Jiang Zhu, Yan Zeng, Jianqi Li, Shujuan Tian, Haolin Liu","doi":"10.1049/ipr2.12115","DOIUrl":"https://doi.org/10.1049/ipr2.12115","url":null,"abstract":"Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real-world sce-narios.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78649737","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}
Asma Shamsi Koshki, M. Ahmadzadeh, M. Zekri, S. Sadri, E. Mahmoudzadeh
Various level-set methods have been suggested for segmenting images with intensity inhomogeneity as local region-based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region-based active contour model called local self-weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well-known local level-set methods which show the perfect capability of the model. The segmentation results were evaluated using the F-score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F-score, precision and recall criteria on the segmentation of breast thermography images, respectively.
{"title":"A level-set method for inhomogeneous image segmentation with application to breast thermography images","authors":"Asma Shamsi Koshki, M. Ahmadzadeh, M. Zekri, S. Sadri, E. Mahmoudzadeh","doi":"10.1049/ipr2.12116","DOIUrl":"https://doi.org/10.1049/ipr2.12116","url":null,"abstract":"Various level-set methods have been suggested for segmenting images with intensity inhomogeneity as local region-based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region-based active contour model called local self-weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well-known local level-set methods which show the perfect capability of the model. The segmentation results were evaluated using the F-score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F-score, precision and recall criteria on the segmentation of breast thermography images, respectively.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91236276","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}
A. Parihar, Kavinder Singh, Hrithik Rohilla, G. Asnani
Low-light image enhancement is a challenging field in image processing. Retinex-based methods perform well for low-light images. However, reflectance and illumination estimation is an ill-posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low-light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi-scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low-light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low-light image enhancement methods. The proposed method provides colour constancy in low-light image enhancement and preserves the naturalness of the image.
{"title":"Fusion-based simultaneous estimation of reflectance and illumination for low-light image enhancement","authors":"A. Parihar, Kavinder Singh, Hrithik Rohilla, G. Asnani","doi":"10.1049/ipr2.12114","DOIUrl":"https://doi.org/10.1049/ipr2.12114","url":null,"abstract":"Low-light image enhancement is a challenging field in image processing. Retinex-based methods perform well for low-light images. However, reflectance and illumination estimation is an ill-posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low-light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi-scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low-light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low-light image enhancement methods. The proposed method provides colour constancy in low-light image enhancement and preserves the naturalness of the image.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77613794","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}
With the development of high-voltage transmission and artificial intelligence technology, unmanned line inspection has become the inevitable trend of current electric power inspection. A new recognition algorithm for high-voltage lines is proposed based on colour (Red, Green, Blue) RGB image to support the unmanned line inspection. Firstly, in order to solve the problem of missing weak edges in image edge detection, an improved Canny algorithm is proposed. Fourier transform Gaussian filter is introduced to enhance the high-frequency signal of the image, which makes the extracted edge information more complete. At the same time, an improved line segment detector (LSD) algorithm is developed to extract the high-voltage line. The complementary edge information of the three channels of the colour RGB image is analyzed, and the calculation formula of the horizontal line angle is improved, which greatly reduces the possibility of false detection and missed detection in the high-voltage line extraction. In addition, the convolution neural network (CNN) is used to accurately recognize the extracted high-voltage lines, which reduces the interference of non–high-voltage lines. Simulation results show that the proposed algorithm has high
{"title":"A new recognition algorithm for high-voltage lines based on improved LSD and convolutional neural networks","authors":"Yanhong Luo, Xue Yu, Dongsheng Yang","doi":"10.1049/ipr2.12031","DOIUrl":"https://doi.org/10.1049/ipr2.12031","url":null,"abstract":"With the development of high-voltage transmission and artificial intelligence technology, unmanned line inspection has become the inevitable trend of current electric power inspection. A new recognition algorithm for high-voltage lines is proposed based on colour (Red, Green, Blue) RGB image to support the unmanned line inspection. Firstly, in order to solve the problem of missing weak edges in image edge detection, an improved Canny algorithm is proposed. Fourier transform Gaussian filter is introduced to enhance the high-frequency signal of the image, which makes the extracted edge information more complete. At the same time, an improved line segment detector (LSD) algorithm is developed to extract the high-voltage line. The complementary edge information of the three channels of the colour RGB image is analyzed, and the calculation formula of the horizontal line angle is improved, which greatly reduces the possibility of false detection and missed detection in the high-voltage line extraction. In addition, the convolution neural network (CNN) is used to accurately recognize the extracted high-voltage lines, which reduces the interference of non–high-voltage lines. Simulation results show that the proposed algorithm has high","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77410191","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 : 2020-09-08DOI: 10.1101/2020.09.05.20181917
H. Danesh, K. Maghooli, R. Kafieh, A. Dehghani
The challenge of limited labeled data in the field of medical imaging and the need for large number of labeled data for training machine learning algorithms, and to measure the performance of image processing algorithms increases the demand to use synthetic images. The purpose of this paper is to construct synthetic and labeled Optical Coherence Tomography (OCT) data to solve the problems like having access to the accurate labeled data and evaluating the processing algorithms. In this study, a modified active shape model is used which considers the anatomical features of available images such as number and thickness of the layers and their associated brightness, the retinal blood vessels, and shadow information with wise consideration of speckle noise. The algorithm is also able to provide different datasets with varying noise level. The validity of our method for synthesis of retinal images is measured by two methods (qualitative assessment and quantitative analysis).
{"title":"Automatic Production of Synthetic Labeled OCT Images Using Active Shape Model","authors":"H. Danesh, K. Maghooli, R. Kafieh, A. Dehghani","doi":"10.1101/2020.09.05.20181917","DOIUrl":"https://doi.org/10.1101/2020.09.05.20181917","url":null,"abstract":"The challenge of limited labeled data in the field of medical imaging and the need for large number of labeled data for training machine learning algorithms, and to measure the performance of image processing algorithms increases the demand to use synthetic images. The purpose of this paper is to construct synthetic and labeled Optical Coherence Tomography (OCT) data to solve the problems like having access to the accurate labeled data and evaluating the processing algorithms. In this study, a modified active shape model is used which considers the anatomical features of available images such as number and thickness of the layers and their associated brightness, the retinal blood vessels, and shadow information with wise consideration of speckle noise. The algorithm is also able to provide different datasets with varying noise level. The validity of our method for synthesis of retinal images is measured by two methods (qualitative assessment and quantitative analysis).","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87347791","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 : 2018-10-17DOI: 10.1049/IET-IPR.2018.5164
Yunyun Yang, W. Jia
Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two-phase model, which is then extended to the multi-phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.
{"title":"Efficient and robust segmentation and correction model for medical images","authors":"Yunyun Yang, W. Jia","doi":"10.1049/IET-IPR.2018.5164","DOIUrl":"https://doi.org/10.1049/IET-IPR.2018.5164","url":null,"abstract":"Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two-phase model, which is then extended to the multi-phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85581787","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 : 2009-12-04DOI: 10.1049/IET-IPR.2008.0201
R. Martins, Catarina Brites, J. Ascenso, F. Pereira
Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63-dB in the overall for sequences with high motion content when large group of pictures are used.
{"title":"Adaptive deblocking filter for transform domain Wyner-Ziv video coding","authors":"R. Martins, Catarina Brites, J. Ascenso, F. Pereira","doi":"10.1049/IET-IPR.2008.0201","DOIUrl":"https://doi.org/10.1049/IET-IPR.2008.0201","url":null,"abstract":"Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63-dB in the overall for sequences with high motion content when large group of pictures are used.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84968513","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 : 2009-12-04DOI: 10.1049/IET-IPR.2008.0195
W. Weerakkody, W. Fernando, A. Kondoz
Distributed video coding (DVC) is an emerging video coding technology that utilises the distributed source coding principles to build very low cost video encoders, yet with remarkable error resilience. In the most common DVC framework, the reconstruction function plays a vital role that has a direct impact on the output video quality. In this study, a novel algorithm is proposed for the reconstruction function, particularly focusing on a unidirectional DVC architecture. The proposed technique exploits the variations of the bit error rate of the Wyner-Ziv decoded bit stream and the assumed noise model in the side information stream. The simulation results show that the proposed algorithm yields a significant improvement of the objective and subjective video quality at no additional bit rate cost.
{"title":"Enhanced reconstruction algorithm for unidirectional distributed video coding","authors":"W. Weerakkody, W. Fernando, A. Kondoz","doi":"10.1049/IET-IPR.2008.0195","DOIUrl":"https://doi.org/10.1049/IET-IPR.2008.0195","url":null,"abstract":"Distributed video coding (DVC) is an emerging video coding technology that utilises the distributed source coding principles to build very low cost video encoders, yet with remarkable error resilience. In the most common DVC framework, the reconstruction function plays a vital role that has a direct impact on the output video quality. In this study, a novel algorithm is proposed for the reconstruction function, particularly focusing on a unidirectional DVC architecture. The proposed technique exploits the variations of the bit error rate of the Wyner-Ziv decoded bit stream and the assumed noise model in the side information stream. The simulation results show that the proposed algorithm yields a significant improvement of the objective and subjective video quality at no additional bit rate cost.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78837909","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 : 2009-12-04DOI: 10.1049/IET-IPR.2008.0202
José Luis Martínez, G. Fernández-Escribano, H. Kalva, P. Cuenca
The authors develop a decoder/encoder system (transcoder) to solve the consumption constraint in the communications between end-user devices, when a new Wyner-Ziv (WZ)/H.264 framework is defined for being used in mobile-to-mobile environments. This approach is based on leaving to the devices only WZ video encoding and traditional video decoding; the lowest complexity algorithms in both paradigms. The system shifts the burden of complexity to the network, where an improved transcoder that reuses information between both paradigms is allocated. The WZ decoding motion vectors are used to reduce the H.264 motion estimation process. The proposed transcoder offers a complexity reduction up to 60- on average, without any rate distortion drop.
{"title":"Motion vector refinement in a Wyner-Ziv to H.264 transcoder for mobile telephony","authors":"José Luis Martínez, G. Fernández-Escribano, H. Kalva, P. Cuenca","doi":"10.1049/IET-IPR.2008.0202","DOIUrl":"https://doi.org/10.1049/IET-IPR.2008.0202","url":null,"abstract":"The authors develop a decoder/encoder system (transcoder) to solve the consumption constraint in the communications between end-user devices, when a new Wyner-Ziv (WZ)/H.264 framework is defined for being used in mobile-to-mobile environments. This approach is based on leaving to the devices only WZ video encoding and traditional video decoding; the lowest complexity algorithms in both paradigms. The system shifts the burden of complexity to the network, where an improved transcoder that reuses information between both paradigms is allocated. The WZ decoding motion vectors are used to reduce the H.264 motion estimation process. The proposed transcoder offers a complexity reduction up to 60- on average, without any rate distortion drop.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73815364","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 : 2009-12-04DOI: 10.1049/IET-IPR.2008.0207
Wei-Jung Chien, Lina Karam
This study presents a transform-domain distributed video coding (DVC) system with a rate-distortion (R-D)-based Adaptive QuanTisation (AQT) scheme. In the proposed system, the transform-domain Wyner-Ziv frame is divided into partitions and is adaptively quantised based on estimated local R-D characteristics for each partition. The R-D estimation is performed based on a correlation model between the original source information and the side information and can be applied at the decoder without adding complexity to the encoder. Coding results and comparisons with existing DVC schemes and with H.264/AVC interframe and intraframe coding are presented to illustrate the performance of the proposed system.
{"title":"Transform-domain distributed video coding with rate-distortion-based adaptive quantisation","authors":"Wei-Jung Chien, Lina Karam","doi":"10.1049/IET-IPR.2008.0207","DOIUrl":"https://doi.org/10.1049/IET-IPR.2008.0207","url":null,"abstract":"This study presents a transform-domain distributed video coding (DVC) system with a rate-distortion (R-D)-based Adaptive QuanTisation (AQT) scheme. In the proposed system, the transform-domain Wyner-Ziv frame is divided into partitions and is adaptively quantised based on estimated local R-D characteristics for each partition. The R-D estimation is performed based on a correlation model between the original source information and the side information and can be applied at the decoder without adding complexity to the encoder. Coding results and comparisons with existing DVC schemes and with H.264/AVC interframe and intraframe coding are presented to illustrate the performance of the proposed system.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83148775","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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