Pub Date : 2014-12-01DOI: 10.1109/VCIP.2014.7051590
Wolfgang Schnurrer, Tobias Tröger, T. Richter, Jürgen Seiler, André Kaup
Lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with peak rate saving of 1.1%.
{"title":"Efficient lossless coding of highpass bands from block-based motion compensated wavelet lifting using JPEG 2000","authors":"Wolfgang Schnurrer, Tobias Tröger, T. Richter, Jürgen Seiler, André Kaup","doi":"10.1109/VCIP.2014.7051590","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051590","url":null,"abstract":"Lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with peak rate saving of 1.1%.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133804977","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051495
Wen-Jiin Tsai, Yi-Shih Liu
Since human vision has much greater resolutions at the center of our visual field than elsewhere, different criteria of quality assessment should be applied on the image areas with different visual resolutions. This paper proposed a foveation-based image quality assessment method which adopted different sizes of windows in quality assessment for a single image. Visual salience models which estimate visual attention regions are used to determine the foveation center and foveation resolution models are used to guide the selection of window sizes for the areas over spatial extent of the image. Finally, the quality scores obtained from different window sizes are pooled together to get a single value for the image. The proposed method has been applied to IQA metrics, SSIM, PSNR, and UQI. The result shows that both Spearman and Kendall correlation coefficients can be improved significantly by our foveation-based method.
{"title":"Foveation-based image quality assessment","authors":"Wen-Jiin Tsai, Yi-Shih Liu","doi":"10.1109/VCIP.2014.7051495","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051495","url":null,"abstract":"Since human vision has much greater resolutions at the center of our visual field than elsewhere, different criteria of quality assessment should be applied on the image areas with different visual resolutions. This paper proposed a foveation-based image quality assessment method which adopted different sizes of windows in quality assessment for a single image. Visual salience models which estimate visual attention regions are used to determine the foveation center and foveation resolution models are used to guide the selection of window sizes for the areas over spatial extent of the image. Finally, the quality scores obtained from different window sizes are pooled together to get a single value for the image. The proposed method has been applied to IQA metrics, SSIM, PSNR, and UQI. The result shows that both Spearman and Kendall correlation coefficients can be improved significantly by our foveation-based method.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"25 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122505","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051587
Yangguang Li, Lei Zhang, Yongbing Zhang, Huiming Xuan, Qionghai Dai
In this paper, we propose a new approach to solve the depth map super-resolution (SR) and denoising problems simultaneously. Inspired by joint-bilateral-upsampling (JBU), we devised the joint-trilateral-upsampling (JTU), which takes edge of the initial depth map, texture of the corresponding high-resolution color image and the values of the surrounding depth pixels, into consideration during the process of SR. To preserve the sharp edge of the up-sampled depth map and remove the noise, we introduce an iterative implementation, where current up-sampled depth map is fed into the next iteration, to refine the filter coefficients of JTU. The iterative JTU presents a high performance at many aspects such as sharping edge, denoising and none texture copying, etc. To demonstrate the superiority of the proposed method, we carry out various experiments and show an across-the-board quality improvement by both of subjective and objective evaluations compared with previous state-of-art methods.
{"title":"Depth map super-resolution via iterative joint-trilateral-upsampling","authors":"Yangguang Li, Lei Zhang, Yongbing Zhang, Huiming Xuan, Qionghai Dai","doi":"10.1109/VCIP.2014.7051587","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051587","url":null,"abstract":"In this paper, we propose a new approach to solve the depth map super-resolution (SR) and denoising problems simultaneously. Inspired by joint-bilateral-upsampling (JBU), we devised the joint-trilateral-upsampling (JTU), which takes edge of the initial depth map, texture of the corresponding high-resolution color image and the values of the surrounding depth pixels, into consideration during the process of SR. To preserve the sharp edge of the up-sampled depth map and remove the noise, we introduce an iterative implementation, where current up-sampled depth map is fed into the next iteration, to refine the filter coefficients of JTU. The iterative JTU presents a high performance at many aspects such as sharping edge, denoising and none texture copying, etc. To demonstrate the superiority of the proposed method, we carry out various experiments and show an across-the-board quality improvement by both of subjective and objective evaluations compared with previous state-of-art methods.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114949321","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051542
N.P. Borg, C. J. Debono, D. Zammit-Mangion
With the availability and rapid advancement of low-cost, low-power, and high-performance processors, machine vision is gaining popularity in various fields, including that of autonomous navigation systems. Applying feature extraction techniques on the captured images provides rich information about the surrounding environment that can be used to accurately determine the position, velocity, and orientation of a vehicle. To extract these features in such an application, we developed the Single Octave Scale Invariant Feature Transform (Single Octave SIFT). This solution drastically reduces the computational load and memory bandwidth requirements while providing an accurate image-based terrain referenced navigation system for micro- and small-sized Unmanned Aerial Vehicles (UAVs). The Gaussian filtering and Keypoint extraction stages are the most computationally intensive parts of the Single Octave SIFT. The main focus of this paper is the design of this modified SIFT algorithm and the basic building blocks needed to implement these two stages within a low-cost, low-power and small footprint Xilinx Spartan-6 LX150 FPGA. Simulation results show that the number of memory accesses is reduced by 99.7% for Full-HD (1920×1080) images1. The operation cycles of the Gaussian filtering and Keypoint extraction stages are reduced by 90.2% and 95% respectively, compared with the single instruction multiple data (SIMD) architecture.
{"title":"A single octave SIFT algorithm for image feature extraction in resource limited hardware systems","authors":"N.P. Borg, C. J. Debono, D. Zammit-Mangion","doi":"10.1109/VCIP.2014.7051542","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051542","url":null,"abstract":"With the availability and rapid advancement of low-cost, low-power, and high-performance processors, machine vision is gaining popularity in various fields, including that of autonomous navigation systems. Applying feature extraction techniques on the captured images provides rich information about the surrounding environment that can be used to accurately determine the position, velocity, and orientation of a vehicle. To extract these features in such an application, we developed the Single Octave Scale Invariant Feature Transform (Single Octave SIFT). This solution drastically reduces the computational load and memory bandwidth requirements while providing an accurate image-based terrain referenced navigation system for micro- and small-sized Unmanned Aerial Vehicles (UAVs). The Gaussian filtering and Keypoint extraction stages are the most computationally intensive parts of the Single Octave SIFT. The main focus of this paper is the design of this modified SIFT algorithm and the basic building blocks needed to implement these two stages within a low-cost, low-power and small footprint Xilinx Spartan-6 LX150 FPGA. Simulation results show that the number of memory accesses is reduced by 99.7% for Full-HD (1920×1080) images1. The operation cycles of the Gaussian filtering and Keypoint extraction stages are reduced by 90.2% and 95% respectively, compared with the single instruction multiple data (SIMD) architecture.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857632","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051563
Fabiane Rediess, R. Conceição, B. Zatt, M. Porto, L. Agostini
This work presents a hardware design for the Sample Adaptive Offset filter, which is an innovation brought by the new video coding standard HEVC. The architectures focus on the encoder side and include both classification methods used in SAO, the Band Offset and Edge Offset, and also the statistical calculations for the offset generation. The proposed architectures feature two sample buffers, classification units for both SAO types and the statistical collection unit. The architectures were described in VHDL and synthesized to an Altera Stratix V FPGA. The synthesis results show that the proposed architectures achieve 364MHz and are capable to process 44 QFHD (3840×2160) frames per second using 8,040 ALUTs of the target device hardware resources.
本文提出了一种基于新视频编码标准HEVC的采样自适应偏移滤波器的硬件设计。该体系结构侧重于编码器端，包括SAO中使用的两种分类方法，频带偏移和边缘偏移，以及偏移生成的统计计算。所建议的体系结构具有两个样本缓冲区、SAO类型的分类单元和统计收集单元。用VHDL描述了这些结构，并将其合成到Altera Stratix V FPGA上。综合结果表明，所提出的架构达到364MHz，能够在目标设备硬件资源的8040个alut上每秒处理44个QFHD (3840×2160)帧。
{"title":"Sample adaptive offset filter hardware design for HEVC encoder","authors":"Fabiane Rediess, R. Conceição, B. Zatt, M. Porto, L. Agostini","doi":"10.1109/VCIP.2014.7051563","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051563","url":null,"abstract":"This work presents a hardware design for the Sample Adaptive Offset filter, which is an innovation brought by the new video coding standard HEVC. The architectures focus on the encoder side and include both classification methods used in SAO, the Band Offset and Edge Offset, and also the statistical calculations for the offset generation. The proposed architectures feature two sample buffers, classification units for both SAO types and the statistical collection unit. The architectures were described in VHDL and synthesized to an Altera Stratix V FPGA. The synthesis results show that the proposed architectures achieve 364MHz and are capable to process 44 QFHD (3840×2160) frames per second using 8,040 ALUTs of the target device hardware resources.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514389","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051619
Jin Zhi, T. Tillo, Fei Cheng
Planar surface is a common feature in man-made structure, thus accurate detection of planar surface can benefit the image/video segmentation and reconstruction and also the navigation system of robots. Since depth map represents the distance from one object to the capturing camera in a grey image, it also can represent the surface characteristics of the objects. So in this paper, we propose a novel Depth-map Driven Planar Surface Detection (DDPSD) method, where detection starts from "the most flat" seed patch on the depth map and uses dynamic threshold value and surface function to control the growing process. Compared with one of the popular planar surface detection algorithms, RANdom SAmples Consensus (RANSAC), the accuracy of the proposed method is obviously superior on typical indoor scenes. Moreover, semi-planar surfaces can be also successfully detected by the proposed method.
{"title":"Depth-map driven planar surfaces detection","authors":"Jin Zhi, T. Tillo, Fei Cheng","doi":"10.1109/VCIP.2014.7051619","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051619","url":null,"abstract":"Planar surface is a common feature in man-made structure, thus accurate detection of planar surface can benefit the image/video segmentation and reconstruction and also the navigation system of robots. Since depth map represents the distance from one object to the capturing camera in a grey image, it also can represent the surface characteristics of the objects. So in this paper, we propose a novel Depth-map Driven Planar Surface Detection (DDPSD) method, where detection starts from \"the most flat\" seed patch on the depth map and uses dynamic threshold value and surface function to control the growing process. Compared with one of the popular planar surface detection algorithms, RANdom SAmples Consensus (RANSAC), the accuracy of the proposed method is obviously superior on typical indoor scenes. Moreover, semi-planar surfaces can be also successfully detected by the proposed method.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132565288","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051580
Tamara Seybold, F. Kuhn, Julian Habigt, Mark Hartenstein, W. Stechele
State-of-the-art denoising methods provide denoising results that can be considered close to optimal. The denoising methods usually have one or more parameters regulating denoising strength that can be adapted for a specific image. To obtain the optimal denoising result, the correct parameter setting is crucial. In this paper, we therefore propose a method that can automatically estimate the optimal parameter of a denoising algorithm. Our approach compares the gradient histogram of a denoised image to an estimated reference gradient histogram. The reference gradient histogram is estimated based on down- and upsampling of the noisy image, thus our method works without a reference and is image-adaptive. We evaluate our propsed down-/upsampling-based gradient histogram method (DUG) based on a subjective test with 20 participants. In the test data, we included images from both the Kodak data set and the more realistic ARRI data set and we used the state-of-the-art denoising method BM3D. Based on the test results we can show that the parameter estimated by our method is very close to the human perception. Despite being very fast and simple to implement, our method shows a lower error than all other suitable no-reference metrics we found.
{"title":"Automatic denoising parameter estimation using gradient histograms","authors":"Tamara Seybold, F. Kuhn, Julian Habigt, Mark Hartenstein, W. Stechele","doi":"10.1109/VCIP.2014.7051580","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051580","url":null,"abstract":"State-of-the-art denoising methods provide denoising results that can be considered close to optimal. The denoising methods usually have one or more parameters regulating denoising strength that can be adapted for a specific image. To obtain the optimal denoising result, the correct parameter setting is crucial. In this paper, we therefore propose a method that can automatically estimate the optimal parameter of a denoising algorithm. Our approach compares the gradient histogram of a denoised image to an estimated reference gradient histogram. The reference gradient histogram is estimated based on down- and upsampling of the noisy image, thus our method works without a reference and is image-adaptive. We evaluate our propsed down-/upsampling-based gradient histogram method (DUG) based on a subjective test with 20 participants. In the test data, we included images from both the Kodak data set and the more realistic ARRI data set and we used the state-of-the-art denoising method BM3D. Based on the test results we can show that the parameter estimated by our method is very close to the human perception. Despite being very fast and simple to implement, our method shows a lower error than all other suitable no-reference metrics we found.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131693924","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051607
Suk kyu Lee, Mungyu Bae, A. Y. Chung, Hwangnam Kim
Many users want to preserve their visual record of the moment that they want to commemorate. Nonetheless, it is still challenging to remember the actual emotional feeling for that moment even by looking at the old picture. There are methods such as to tag or hide the message within the image. However, tradeoffs exist by attaching additional data for the former method and the quality of the image is degraded for the latter one. It is difficult to avoid these two tradeoffs. In this paper, we propose D-mago to preserve the moment to remember as an image, which is consists of the visual information and the emotional feeling without binding extra data or degrading the quality of the image. To further verify the benefit of our proposed algorithm, we conducted series of evaluation studies to see the effectiveness of the proposed scheme. The results indicate that D-mago overcomes the preceding tradeoffPs by maintaining PSNR above 40 dB.
{"title":"D-mago: A novel visual entity for storing emotional feeling with visual imprint","authors":"Suk kyu Lee, Mungyu Bae, A. Y. Chung, Hwangnam Kim","doi":"10.1109/VCIP.2014.7051607","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051607","url":null,"abstract":"Many users want to preserve their visual record of the moment that they want to commemorate. Nonetheless, it is still challenging to remember the actual emotional feeling for that moment even by looking at the old picture. There are methods such as to tag or hide the message within the image. However, tradeoffs exist by attaching additional data for the former method and the quality of the image is degraded for the latter one. It is difficult to avoid these two tradeoffs. In this paper, we propose D-mago to preserve the moment to remember as an image, which is consists of the visual information and the emotional feeling without binding extra data or degrading the quality of the image. To further verify the benefit of our proposed algorithm, we conducted series of evaluation studies to see the effectiveness of the proposed scheme. The results indicate that D-mago overcomes the preceding tradeoffPs by maintaining PSNR above 40 dB.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130940934","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051523
G. Sanchez, Mário Saldanha, Gabriel Balota, B. Zatt, M. Porto, L. Agostini
This paper proposes a complexity reduction algorithm for the depth maps intra prediction of the emerging 3D High Efficiency Video Coding standard (3D-HEVC). The 3D-HEVC introduces a new set of specific tools for the depth map coding that includes four Depth Modeling Modes (DMM) and these new features have inserted extra effort on the intra prediction. This extra effort is undesired and contributes to increasing the power consumption, which is a huge problem especially for embedded-systems. For this reason, this paper proposes a complexity reduction algorithm for the DMM 1, called Gradient-Based Mode One Filter (GMOF). This algorithm applies a filter to the borders of the encoded block and determines the best positions to evaluate the DMM 1, reducing the computational effort of DMM 1 process. Experimental analysis showed that GMOF is capable to achieve, in average, a complexity reduction of 9.8% on depth maps prediction, when evaluating under Common Test Conditions (CTC), with minor impacts on the quality of the synthesized views.
针对新兴的3D高效视频编码标准(3D- hevc)，提出了一种深度图帧内预测的复杂度降低算法。3D-HEVC为深度图编码引入了一套新的特定工具，其中包括四种深度建模模式(DMM)，这些新功能为图像内预测增加了额外的工作量。这种额外的工作是不希望的，并且会增加功耗，这是一个巨大的问题，特别是对于嵌入式系统。为此，本文提出了一种dmm1的复杂度降低算法，称为基于梯度的模式一滤波器(GMOF)。该算法对编码块的边界进行滤波，确定对DMM - 1进行评估的最佳位置，减少了DMM - 1过程的计算量。实验分析表明，在通用测试条件(Common Test Conditions, CTC)下评估时，GMOF能够将深度图预测的复杂性平均降低9.8%，对合成视图的质量影响较小。
{"title":"A complexity reduction algorithm for depth maps intra prediction on the 3D-HEVC","authors":"G. Sanchez, Mário Saldanha, Gabriel Balota, B. Zatt, M. Porto, L. Agostini","doi":"10.1109/VCIP.2014.7051523","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051523","url":null,"abstract":"This paper proposes a complexity reduction algorithm for the depth maps intra prediction of the emerging 3D High Efficiency Video Coding standard (3D-HEVC). The 3D-HEVC introduces a new set of specific tools for the depth map coding that includes four Depth Modeling Modes (DMM) and these new features have inserted extra effort on the intra prediction. This extra effort is undesired and contributes to increasing the power consumption, which is a huge problem especially for embedded-systems. For this reason, this paper proposes a complexity reduction algorithm for the DMM 1, called Gradient-Based Mode One Filter (GMOF). This algorithm applies a filter to the borders of the encoded block and determines the best positions to evaluate the DMM 1, reducing the computational effort of DMM 1 process. Experimental analysis showed that GMOF is capable to achieve, in average, a complexity reduction of 9.8% on depth maps prediction, when evaluating under Common Test Conditions (CTC), with minor impacts on the quality of the synthesized views.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130983834","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 : 2014-12-01DOI: 10.1109/VCIP.2014.7051569
Haifeng Dong, Bojin Zhuang, Fei Su, Zhicheng Zhao
Distributed compressive video sensing (DCVS) is a new emerging video codec that incorporates advantages of distributed video coding (DVC) and compressive sensing (CS). However, due to the absence of a good sparse basis, the DCVS does not achieve ideal compressing efficiency compared with the traditional video codec, such as MPEG-4, H.264, etc. This paper proposes a new hybrid sparse basis, which combines the image-block prediction and DCT basis. Adaptive block-based prediction is employed to learn block-prediction basis by exploiting temporal correlation among successive frames. Based on linear DCT basis and predicted basis, the hybrid sparse basis can achieve sparser representation with lower complexity. The experiment results indicate that the proposal outperforms the state-of-the-art DCVS schemes on both visual quality and average PSNR. In addition, an iterative fashion proposed in the decoder can enhance the sparsity of the hybrid sparse basis and improve the rate-distortion performance significantly.
{"title":"A novel distributed compressive video sensing based on hybrid sparse basis","authors":"Haifeng Dong, Bojin Zhuang, Fei Su, Zhicheng Zhao","doi":"10.1109/VCIP.2014.7051569","DOIUrl":"https://doi.org/10.1109/VCIP.2014.7051569","url":null,"abstract":"Distributed compressive video sensing (DCVS) is a new emerging video codec that incorporates advantages of distributed video coding (DVC) and compressive sensing (CS). However, due to the absence of a good sparse basis, the DCVS does not achieve ideal compressing efficiency compared with the traditional video codec, such as MPEG-4, H.264, etc. This paper proposes a new hybrid sparse basis, which combines the image-block prediction and DCT basis. Adaptive block-based prediction is employed to learn block-prediction basis by exploiting temporal correlation among successive frames. Based on linear DCT basis and predicted basis, the hybrid sparse basis can achieve sparser representation with lower complexity. The experiment results indicate that the proposal outperforms the state-of-the-art DCVS schemes on both visual quality and average PSNR. In addition, an iterative fashion proposed in the decoder can enhance the sparsity of the hybrid sparse basis and improve the rate-distortion performance significantly.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128449057","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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