A segmentation map, either static or dynamic, refers to a two-dimensional picture that may vary with time and indicates the segmentation label per pixel. Both the semantic map and the occupancy map in video-based poin...
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A segmentation map, either static or dynamic, refers to a two-dimensional picture that may vary with time and indicates the segmentation label per pixel. Both the semantic map and the occupancy map in video-based point cloud compression (V-PCC) belong to the segmentation map we referred to. The semantic map can work for many machine vision tasks like tracking and has been used as a layer of image representation in some image compression methods. The occupancy map constitutes a part of the point cloud coding bitstream. Since segmentation maps are widely used, how to efficiently compress them is of interest. We propose a segmentation map lossless compression scheme namely CC-SMC, exploiting the nature of segmentation maps that usually contain limited colors and sharp edges. Specifically, we design a chain coding-based scheme combined with quadtree-based block partitioning. For intraframe coding, one block is partitioned recursively with a quadtree structure, until the block contains only one color, is smaller than a threshold, or satisfies the defined chain coding condition. We revise the three-orthogonal chain coding method to incorporate contextual information and design effective intraframe prediction methods. For interframe coding, one block may find a reference block;the chain difference between the current and the reference blocks is coded. We implement the proposed scheme and test it on several different kinds of segmentation maps. Compared with advanced lossless image compression techniques, our proposed scheme obtains more than 10% bits reduction as well as more than 20% decoding time-saving. The code is available at https://***/Yang-Runyu/CC-SMC.
In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a v...
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In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack-McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms' results and the outcomes from all the other approaches are statistically significant. (C) 2019 Elsevier B.V. All rights reserved.
Thrifty methods to represent and store three dimensional objects are important. Two different methods for describing voxel-based objects (VBOs) by means of edging (ETs) and intersecting (ITs) trees are demonstrated. E...
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Thrifty methods to represent and store three dimensional objects are important. Two different methods for describing voxel-based objects (VBOs) by means of edging (ETs) and intersecting (ITs) trees are demonstrated. Each tree comes from a different kind of border of the underlying VBO, and both trees are one dimensional alternative descriptors to skeletons for VBOs representation. Vertices in the trees correspond to the vertices of the VBO enclosing surface where some surface vertices have been conveniently suppressed. These descriptors are computed using a base-five digit chain code (combined with parentheses) and has been used to illustrate three dimensional curves and enclosing trees. The descriptors are invariant under rotation and translation, and preserve the VBO shape. Using either descriptor, the description of the mirror image of a VBO is easily obtained. The proposed descriptor notation is a good tool for storing VBOs, and intersecting trees providing further storage savings. Enclosing trees (EcTs) are briefly reviewed as a preamble to introduce ETs and ITs.
In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is direc...
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ISBN:
(纸本)9781728180687
In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.
Learned image compression has shown remarkable compression efficiency gain over the traditional image compression solutions, which is partially attributed to the learned entropy models and the adopted entropy coding e...
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Learned image compression has shown remarkable compression efficiency gain over the traditional image compression solutions, which is partially attributed to the learned entropy models and the adopted entropy coding engine. However, the inference of the entropy models and the sequential nature of the entropy coding both incur high time complexity. Meanwhile, the neural network-based entropy models usually involve floating-point computations, which incur inconsistent probability estimation and decoding failure in different platforms. We address these limitations by introducing an efficient and cross-platform entropy coding method, chain coding-based latent compression (CC-LC), into learned image compression. First, we leverage the classic chain coding and carefully design a block-based entropy coding procedure, significantly reducing the number of coding symbols and thus the coding time. Second, since CC-LC is not based on neural networks, we propose a rate estimation network as a surrogate of CC-LC during the end-to-end training. Third, we alternately train the analysis/synthesis networks and the rate estimation network for the rate-distortion optimization, making the learned latent fit CC-LC. Experimental results show that our method achieves much lower time complexity than the other learned image compression methods, ensures cross-platform consistency, and has comparable compression efficiency with BPG. Our code and models are publicly available at https://***/Yang-Runyu/CC-LC.
A measure of tortuosity for 20 curves is presented. Tortuosity is a very important property of curves and has many applications, such as: how to measure the tortuosity of retinal blood vessels, intracerebral vasculatu...
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A measure of tortuosity for 20 curves is presented. Tortuosity is a very important property of curves and has many applications, such as: how to measure the tortuosity of retinal blood vessels, intracerebral vasculature, aluminum foams, etc. The measure of tortuosity proposed here is based on a chain code called Slope chain Code (SCC). The SCC uses some ideas which were described in [A geometric structure for 2D shapes and 3D surfaces, Pattern Recognition 25 (1992)483-496]. The SCC of a curve is obtained by placing straight-line segments of constant length around the curve (the endpoints of the straight-line segments always touching the curve), and calculating the slope changes between contiguous straight-line segments scaled to a continuous range from -1 to 1. The SCC of a curve is independent of translation, rotation, and optionally, of scaling, which is an important advantage for computing tortuosity. Also, the minimum and maximum values of tortuosity for curves and a measure of normalized tortuosity are described. Finally, an application of the proposed measure of tortuosity is presented which corresponds to the computation of retinal blood vessel tortuosity. (C) 2012 Elsevier Ltd. All rights reserved.
Symmetry is an important feature in natural and man-made objects;particularly, mirror symmetry is a relevant task in fields such as computer vision and pattern recognition. In the current work, we propose a new method...
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Symmetry is an important feature in natural and man-made objects;particularly, mirror symmetry is a relevant task in fields such as computer vision and pattern recognition. In the current work, we propose a new method to characterize mirror-symmetry in open and closed curves represented by means of the Slope chain Code. This representation is invariant under scale, rotation, and translation, highly desirable properties for object recognition applications. The proposed method detects symmetries through simple inversion, concatenation and reflection operations on the chains, thus allowing the classification of symmetrical and asymmetrical contours. It also introduces a measure to quantify the degree of symmetry in quasi-mirror-symmetrical objects. Furthermore, it allows the identification of multiple symmetry axes and their location. Results show high performances in symmetrical/asymmetrical classification (0.9 recall, 0.9 accuracy, 0.97 precision) and axes' detection (0.8 recall, 0.84 accuracy, 0.99 precision). Compared to other methods, the proposed algorithm provides properties such as: global, local, and multiple axes' detection, as well as the capability to classify symmetrical objects, which makes it adequate for several practical applications, like the three exemplified in the paper. (C) 2018 Elsevier Ltd. All rights reserved.
Generally speaking, a spiral is a 2D curve which winds about a fixed point. Now, we present a new, alternative, and easy way to describe and generate spirals by means of the use of the Slope chain Code (SCC) [E. Bribi...
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Generally speaking, a spiral is a 2D curve which winds about a fixed point. Now, we present a new, alternative, and easy way to describe and generate spirals by means of the use of the Slope chain Code (SCC) [E. Bribiesca, A measure of tortuosity based on chain coding, Pattern Recognition 46 (2013) 716-724]. Thus, each spiral is represented by only one chain. The chain elements produce a finite alphabet which allows us to use grammatical techniques for spiral classification. Spirals are composed of constant straight-line segments and their chain elements are obtained by calculating the slope changes between contiguous straight-line segments (angle of contingence) scaled to a continuous range from -1 (-180 degrees) to 1 (180 degrees). The SCC notation is invariant under translation, rotation, optionally under scaling, and it does not use a grid. Other interesting properties can be derived from this notation, such as: the mirror symmetry and inverse spirals, the accumulated slope, the slope change mean, and tortuosity for spirals. We introduce new concepts of projective polygonal paths and osculating polygons. We present a new spiral called the SCC polygonal spiral and its chain which is described by the numerical sequence for n >= 3, to the best of our knowledge this is the first time that this spiral and its chain are presented. The importance of this spiral and its chain is that this chain is covering all the slope changes of all the regular polygons composed of n edges (n-gons). Also, we describe the chain which generates the spiral of Archimedes. Finally, we present some results of different kind of spirals from the real world, including spiral patterns in shells. (C) 2019 Elsevier Ltd. All rights reserved.
We present a new approach based on the Slope chain Code to determine whether a curve is rotational symmetrical and its order of symmetry. The proposed approach works for open and closed perfectly symmetrical or quasi-...
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We present a new approach based on the Slope chain Code to determine whether a curve is rotational symmetrical and its order of symmetry. The proposed approach works for open and closed perfectly symmetrical or quasi-symmetrical 2D curves. Simple operations on the SCC and its invariant properties are central to our methodology. To evaluate the proposed methodology, we use 1400 curves from a public database. For the symmetrical/asymmetrical classification task, a recall (R) of 0.86, a balanced accuracy (BA) of 0.92, and a precision (P) of 0.87 were obtained. For the quasi-symmetrical/quasi-asymmetrical classification task, R=0.77, BA=0.83, and P=0.70 were obtained. For the order of rotational symmetry detection task, the following performance was achieved: R=0.97, BA=0.98, and P=0.95 for a symmetrical set of curves, and R=0.98, BA=0.98, and P=0.90 for a quasi-symmetrical set of curves. We conclude our presentation demonstrating the usefulness of our methodology with three practical applications (C) 2020 Elsevier Ltd. All rights reserved.
This paper presents a fast and effective method for generating chain-encoded representations of arbitrary contours of connected regions in a binary image. Furthermore, an optional method is presented allowing the addi...
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ISBN:
(纸本)9781479904020;9781479904037
This paper presents a fast and effective method for generating chain-encoded representations of arbitrary contours of connected regions in a binary image. Furthermore, an optional method is presented allowing the additional concurrent extraction of the topological hierarchy tree within the image. Both parts of the algorithm are executed by means of a single raster scan with a 2x3 neighborhood convolution window. This not only allows the reduction of the pixel buffer to a single image line, it furthermore enables fast neighborhood evaluation by means of a moderately sized look-up-table with 64 entries, to be directly addressed by the six pixel in window focus. With these main characteristics, the algorithm is well suited for efficient parallel processing implementations in streaming applications.
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