Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First,we construct the image energy map using gradient magnitude of the pixels and calculate the accumu...Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First,we construct the image energy map using gradient magnitude of the pixels and calculate the accumulated energy of each column,dividing the image into several strips by integrating similar energy columns. The reduced amount of dimension is decided in inverse proportion to the average energy for each strip. Then we retarget the image combining scaling with cropping in terms of each strip＇s reduced ratio. Experiment results show that the proposed algorithm is capable of implementing fast image retargeting and preserving both the local structures and the global visual effect of the image.展开更多
Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image...Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content- aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.展开更多
Mobile devices are increasingly powerful in multimedia transmitting and browsing. However, the small screen and different aspect ratios of mobile devices lower visual quality while watching images or videos. Video ret...Mobile devices are increasingly powerful in multimedia transmitting and browsing. However, the small screen and different aspect ratios of mobile devices lower visual quality while watching images or videos. Video retargeting is aim at fitting an existing video into arbitrary size and aspect ratio. Previous content-aware retargeting methods are mostly high computational cost, which limits their applications on the portable devices with low computation ability. In this paper, a new crop-and-scale approach is presented to adapt video to better suit the target display. We automatically find the optimal parameters of cropping window using the dynamic programming method,, and then scale it to fit the target display. The cropping window can smoothly shift during a shot to follow the movement＇of important objects. The retargeting results using our approach introduce no deformation and jitter effects over the whole video. Experimental results show the success of our approach on adapting a variety of source videos to small display sizes.展开更多
A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimi...A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization： forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.展开更多
In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing prec...In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.展开更多
Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimizatio...Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.展开更多
We propose a disparity-constrained retargeting method for stereoscopic 3D video, which simultaneously resizes a binocular video to a new aspect ratio and remaps the depth to the perceptual comfort zone. First, we mode...We propose a disparity-constrained retargeting method for stereoscopic 3D video, which simultaneously resizes a binocular video to a new aspect ratio and remaps the depth to the perceptual comfort zone. First, we model distortion energies to prevent important video contents from deforming. Then, to maintain depth mapping stability, we model disparity variation energies to constraint the disparity range both in spatial and temporal domains. The last component of our method is a non-uniform, pixel-wise warp to the target resolution based on these energy models. Using this method, we can process the original stereoscopic video to generate new, high-perceptual-quality versions at different display resolutions. For evaluation, we conduct a user study; we also discuss the performance of our method.展开更多
The numerous works on media retargeting call for a thorough and comprehensive survey for reviewing and categorizing existing works and providing insights that can help future design of retargeting approaches and its a...The numerous works on media retargeting call for a thorough and comprehensive survey for reviewing and categorizing existing works and providing insights that can help future design of retargeting approaches and its applica- tions. First, we present the basic problem of media retarget- ing and detail state-of-the-art retargeting methods devised to solve it. Second, we review recent works on objective quality assessment of media retargeting, where we find that although these works are designed to make the objective assessment result in accordance with the subjective evaluation, they are only suitable for certain situations. Considering the subjective nature of aesthetics, designing objective assessment metric for media retargeting could be a promising area for investiga- tion. Third, we elaborate on other applications extended from retargeting techniques. We show how to apply the retarget- ing techniques in other fields to solve their challenging prob- lems, and reveal that retargeting technique is not just a simple scaling algorithm, but a thought or concept, which has great flexibility and is quite useful. We believe this review can help researchers and practitioners to solve the existing problems of media retargeting and bring new ideas in their works.展开更多
文摘Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First,we construct the image energy map using gradient magnitude of the pixels and calculate the accumulated energy of each column,dividing the image into several strips by integrating similar energy columns. The reduced amount of dimension is decided in inverse proportion to the average energy for each strip. Then we retarget the image combining scaling with cropping in terms of each strip＇s reduced ratio. Experiment results show that the proposed algorithm is capable of implementing fast image retargeting and preserving both the local structures and the global visual effect of the image.
基金supported by“MOST”under Grants No.105-2628-E-224-001-MY3 and No.103-2221-E-224-034-MY2
文摘Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content- aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.
基金supported by the the Science and Technology Commission of Shanghai Municipality(Grant No.11ZR1413000)the Innovation Program for Graduate Students of Shanghai University(Grant No.SHUCX112125)the Key Laboratory for Advanced Displays and System Appliction(Shanghai University),Ministry of Education,China(Grant No.P201004)
文摘Mobile devices are increasingly powerful in multimedia transmitting and browsing. However, the small screen and different aspect ratios of mobile devices lower visual quality while watching images or videos. Video retargeting is aim at fitting an existing video into arbitrary size and aspect ratio. Previous content-aware retargeting methods are mostly high computational cost, which limits their applications on the portable devices with low computation ability. In this paper, a new crop-and-scale approach is presented to adapt video to better suit the target display. We automatically find the optimal parameters of cropping window using the dynamic programming method,, and then scale it to fit the target display. The cropping window can smoothly shift during a shot to follow the movement＇of important objects. The retargeting results using our approach introduce no deformation and jitter effects over the whole video. Experimental results show the success of our approach on adapting a variety of source videos to small display sizes.
文摘A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization： forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.
基金supported by the iMinds visualization research program(HIVIZ)
文摘In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.
文摘Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.
基金supported by the National Basic Research Program of China under Grant No. 2011CB302206the National Natural Science Foundation of China under Grant Nos. 61272226 and 61272231Beijing Key Laboratory of Networked Multimedia
文摘We propose a disparity-constrained retargeting method for stereoscopic 3D video, which simultaneously resizes a binocular video to a new aspect ratio and remaps the depth to the perceptual comfort zone. First, we model distortion energies to prevent important video contents from deforming. Then, to maintain depth mapping stability, we model disparity variation energies to constraint the disparity range both in spatial and temporal domains. The last component of our method is a non-uniform, pixel-wise warp to the target resolution based on these energy models. Using this method, we can process the original stereoscopic video to generate new, high-perceptual-quality versions at different display resolutions. For evaluation, we conduct a user study; we also discuss the performance of our method.
文摘The numerous works on media retargeting call for a thorough and comprehensive survey for reviewing and categorizing existing works and providing insights that can help future design of retargeting approaches and its applica- tions. First, we present the basic problem of media retarget- ing and detail state-of-the-art retargeting methods devised to solve it. Second, we review recent works on objective quality assessment of media retargeting, where we find that although these works are designed to make the objective assessment result in accordance with the subjective evaluation, they are only suitable for certain situations. Considering the subjective nature of aesthetics, designing objective assessment metric for media retargeting could be a promising area for investiga- tion. Third, we elaborate on other applications extended from retargeting techniques. We show how to apply the retarget- ing techniques in other fields to solve their challenging prob- lems, and reveal that retargeting technique is not just a simple scaling algorithm, but a thought or concept, which has great flexibility and is quite useful. We believe this review can help researchers and practitioners to solve the existing problems of media retargeting and bring new ideas in their works.
