To enhance the robustness of video transmission over noisy channels, this paper presents a multiple description video coding algorithm based on chessboard-interpolation. In the algorithm, the input image is decomposed...To enhance the robustness of video transmission over noisy channels, this paper presents a multiple description video coding algorithm based on chessboard-interpolation. In the algorithm, the input image is decomposed according to the chessboard pattern, and then interpolated to produce two approximate images with the same resolution. Consequently, the state-of-the-art DCT+MC (Discrete Cosine Transform + Motion Compensation) video codec is independently applied to the two approximate images to generate two descriptions of the original image. In this framework, a fairely good reconstructed image quality is obtained when two descriptions are received simultaneously, while an acceptable reconstructed image quality could be yielded if only one description is available. Moreover, the mismatch between the encoder and the decoder could be effectively controlled through partial coding of the difference signal between two descriptions. In bidirectional video communications, a drift control scheme is further proposed, in which the error drift could be eliminated after the encoder imitating the error concealment actions of the decoder. Since the inherent correlation among adjacent blocks of DCT+MC video coding is efficiently exploited, this algorithm has a better redundancy-rate-distortion (RRD) performance than other multiple description algorithms. Simulation results show that the proposed algorithm is fairly robust while preserves a high compression rate. A more constant reconstructed image quality is achieved over extremely noisy channels, compared with traditional single description coding. In addition, it is observed that the mismatch and the error drift are effectively controlled.展开更多
In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing ...In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.展开更多
文摘To enhance the robustness of video transmission over noisy channels, this paper presents a multiple description video coding algorithm based on chessboard-interpolation. In the algorithm, the input image is decomposed according to the chessboard pattern, and then interpolated to produce two approximate images with the same resolution. Consequently, the state-of-the-art DCT+MC (Discrete Cosine Transform + Motion Compensation) video codec is independently applied to the two approximate images to generate two descriptions of the original image. In this framework, a fairely good reconstructed image quality is obtained when two descriptions are received simultaneously, while an acceptable reconstructed image quality could be yielded if only one description is available. Moreover, the mismatch between the encoder and the decoder could be effectively controlled through partial coding of the difference signal between two descriptions. In bidirectional video communications, a drift control scheme is further proposed, in which the error drift could be eliminated after the encoder imitating the error concealment actions of the decoder. Since the inherent correlation among adjacent blocks of DCT+MC video coding is efficiently exploited, this algorithm has a better redundancy-rate-distortion (RRD) performance than other multiple description algorithms. Simulation results show that the proposed algorithm is fairly robust while preserves a high compression rate. A more constant reconstructed image quality is achieved over extremely noisy channels, compared with traditional single description coding. In addition, it is observed that the mismatch and the error drift are effectively controlled.
基金supported by the Research on the Reform of Curriculum Assessment Methods for College Mathematics Platform Courses(No.53111104016)。
文摘In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.
