3D studio production of animated actor models

A framework for constructing detailed animated models of an actor's shape and appearance from multiple view images is presented. Multiple views of an actor are captured in a studio with controlled illumination and background. An initial low-resolution approximation of the person's shape is reconstructed by deformation of a generic humanoid model to fit the visual hull using shape constrained optimisation to preserve the surface parameterisation for animation. Stereo reconstruction with multiple view constraints is then used to reconstruct the detailed surface shape. High-resolution shape detail from stereo is represented in a structured format for animation by displacement mapping from the low-resolution model surface. A novel integration algorithm using displacement maps is introduced to combine overlapping stereo surface measurements from multiple views into a single displacement map representation of the high-resolution surface detail. Results of 3D actor modelling in a 14 camera studio demonstrate improved representation of detailed surface shapes, such as creases in clothing, compared to previous model fitting approaches. Actor models can be animated and rendered from arbitrary views under different illumination to produce free-viewpoint video sequences. The proposed framework enables rapid transformation of captured multiple view images into a structured representation suitable for realistic animation.     

Motion-compensated compression of 3D animation models

A new algorithm is proposed to compress animated 3D mesh models. First, an input mesh model is partitioned into segments, and each segment is motion compensated from that of the previous time instance. Then, the motion residuals are effectively compressed by using a transform coding method. It is shown that the proposed algorithm yields a much higher compression ratio than the MPEG-4 codec     

Watermarking 3D models using spectral mesh compression

We propose a robust and imperceptible spectral watermarking method for high rate embedding of a watermark into 3D polygonal meshes. Our approach consists of four main steps: (1) the mesh is partitioned into smaller sub-meshes, and then the watermark embedding and extraction algorithms are applied to each sub-mesh, (2) the mesh Laplacian spectral compression is applied to the sub-meshes, (3) the watermark data is distributed over the spectral coefficients of the compressed sub-meshes, (4) the modified spectral coefficients with some other basis functions are used to obtain uncompressed watermarked 3D mesh. The main attractive features of this approach are simplicity, flexibility in data embedding capacity, and fast implementation. Extensive experimental results show the improved performance of the proposed method, and also its robustness against the most common attacks including the geometric transformations, adaptive random noise, mesh smoothing, mesh cropping, and combinations of these attacks.     

基于GIF动态图像的自适应隐写方案

提出了一种新的针对GIF(graphics interchange format)动态图像的自适应隐写算法。通过图像帧间相同位置像素的颜色亮度方差和颜色平均亮度建立帧间自适应准则,通过帧内的颜色规则与颜色匹配表建立帧内自适应准则,将图像分为可隐写像素与不可隐写像素,并用多帧可隐写像素联合隐写秘密信息。实验结果表明,算法具有良好的隐写效果并且具有较大的隐藏容量,与其他的密写方案相比,本方案保证了未授权者非法提取秘密信息的难度足够大,具有较高的安全性。     

Improved prediction methods for scalable predictive animated mesh compression

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.     

Hardware implementation of a disparity estimation scheme for real-time compression in 3D imaging applications

This paper presents a novel hardware implementation of a disparity estimation scheme targeted to real-time Integral Photography (IP) image and video sequence compression. The software developed for IP image compression achieves high quality ratios over classic methodologies by exploiting the inherent redundancy that is present in IP images. However, there are certain time constraints to the software approach that must be confronted in order to address real-time applications. Our main effort is to achieve real-time performance by implementing in hardware the most time-consuming parts of the compression algorithm. The proposed novel digital architecture features minimized memory read operations and extensive simultaneous processing, while taking into concern the memory and data bandwidth limitations of a single FPGA implementation. Our results demonstrate that the implemented hardware system can successfully process high resolution IP video sequences in real-time, addressing a vast range of applications, from mobile systems to demanding desktop displays.     

Technologies for 3D mesh compression: A survey

Three-dimensional (3D) meshes have been widely used in graphic applications for the representation of 3D objects. They often require a huge amount of data for storage and/or transmission in the raw data format. Since most applications demand compact storage, fast transmission, and efficient processing of 3D meshes, many algorithms have been proposed to compress 3D meshes efficiently since early 1990s. In this survey paper, we examine 3D mesh compression technologies developed over the last decade, with the main focus on triangular mesh compression technologies. In this effort, we classify various algorithms into classes, describe main ideas behind each class, and compare the advantages and shortcomings of the algorithms in each class. Finally, we address some trends in the 3D mesh compression technology development.     

3D scanning-based compression technique for digital hologram video

In this paper, we proposed an efficient coding method for digital hologram video using a three-dimensional (3D) scanning method and two-dimensional (2D) video compression technique. It consists of separation of the captured 3D image into R, G, and B color space components, localization by segmenting the fringe pattern in to M×N [pixel²], frequency-transform by 2D discrete cosine transform (2D DCT), 3D-scanning the segments to form a video sequence, classification of coefficients, and hybrid video coding with H.264/AVC, differential pulse code modulation (DPCM), and lossless coding method. The experimental results with this method showed that the proposed method has compression ratios of 8–16 times higher than the previous researches. Thus, we expect it to contribute to reduce the amount of digital hologram data for communication or storage.     

3D motion estimation for depth information compression in 3D-TV applications

A new approach for predicting and coding depth information in 3D-TV (three-dimensional television) applications is presented. Properties of the depth information, which complements monoscopic video and enables 3D experience, are used in the proposed 3D motion prediction. The new approach leads to more efficient motion compensation and finally to higher compression. Built on the top of the conventional approaches for video coding, the proposed technique is suitable for integration in upcoming 3D-TV products.     

三维激光扫描测量系统的三维图像数据压缩

三维彩色激光扫描测量系统是利用计算机视觉的原理对物体进行三维测量和重建,以获取物体的三维图像。三维图像的数据量很大,文中研究了三维图像的压缩策略和算法,采用三角形条带的网格压缩方法,以及顶点数据的编码方法,可以把三维图像的存贮空间减小到压缩前数据的１／６,从而有效地解决了测量系统在实际应用环境下的图像存贮和传输问题。     

Efficient interpolation scheme for image sequence data compression

Presents a video coding approach that requires a very low bit rate and achieves good visual quality. The approach allows easy and cheap hardware implementation. Intra- and interframe correlations are fully exploited through a spatio-temporal interpolation applied to a nonuniform 3D grid     

A high-quality fixed-length compression scheme for color images

We present a new compression method which compresses 8×8 picture blocks by fixed-length codewords. The compression operation is performed on the discrete cosine transforms, DCT, of each block. As a result, our method combines the distinct advantage of being fixed-length with the high image quality obtained by the DCT based compression methods. Our method has excellent error-resistance characteristics since it does not have the synchronization and error propagation problems inherent in variable-length coding methods     

High compression vector quantisation scheme for image coding

An address-predicted vector quantiser (APVQ) is proposed for image coding, which exploits correlation among intervectors by means of predicting. Compared with the general vector quantiser, the APVQ can obtain higher compression ratio, while keeping the same picture quality.<>     

A linear quadtree compression scheme for image encryption

A private key encryption scheme for a two-dimensional image data is proposed in this work. This scheme is designed on the basis of lossless data compression principle. The proposed scheme is developed to have both data encryption and compression performed simultaneously. For the lossless data compression effect, the quadtree data structure is used to represent the image; for the encryption purpose, various scanning sequences of image data are provided. The scanning sequences comprise a private key for encryption. Twenty four possible combinations of scanning sequences are defined for accessing four quadrants, thereby making available 24ⁿ × 4^{n(n − 1)/2} possibilities to encode an image of resolution 2ⁿ × 2ⁿ. The security of the proposed encryption scheme therefore relies on the computational infeasibility of an exhaustive search approach. Three images of 512 × 512 pixels are used to verify the feasibility of the proposed scheme. The testing results and analysis demonstrate the characteristics of the proposed scheme. This scheme can be applied for problems of data storage or transmission in a public network.     

LOFT: A low-overhead fault-tolerant routing scheme for 3D NoCs

As one of the main trends of communication technology for 3D integrated circuits, the 3D networks-on-chip (NoCs) have drawn high concern from the academia. The links are main components of the NoCs. For the permanent link faults, the fault-tolerant routing scheme has been regarded as an effective mechanism to ensure the performance of the 2D NoCs. In this paper, we propose a low-overhead fault-tolerant routing scheme called LOFT for 3D Mesh NoCs without requiring any virtual channels (VCs). LOFT is a deadlock-free scheme by adopting a logic-based routing named LBDRe² guided by a turn model Complete-OE. The experimental results show that LOFT possesses better performance, improved reliability and lower overhead compared with the state-of-the-art reliable routing schemes.     

Fast codevector search scheme for 3D mesh model vector quantisation

A new fast approach to the nearest codevector search for 3D mesh compression using an orthonormal transformed codebook is proposed. The algorithm uses the coefficients of an input vector along a set of orthonormal bases as the criteria to reject impossible codevectors. Compared to the full search algorithm, a great deal of computational time is saved without extra distortion and additional storage requirement. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of computational complexity.     

Image compression optimized for 3D reconstruction by utilizing deep neural networks

Computer vision tasks are often expected to be executed on compressed images. Classical image compression standards like JPEG 2000 are widely used. However, they do not account for the specific end-task at hand. Motivated by works on recurrent neural network (RNN)-based image compression and three-dimensional (3D) reconstruction, we propose unified network architectures to solve both tasks jointly. These joint models provide image compression tailored for the specific task of 3D reconstruction. Images compressed by our proposed models, yield 3D reconstruction performance superior as compared to using JPEG 2000 compression. Our models significantly extend the range of compression rates for which 3D reconstruction is possible. We also show that this can be done highly efficiently at almost no additional cost to obtain compression on top of the computation already required for performing the 3D reconstruction task.     

A prediction-based neural network scheme for lossless data compression

This paper proposes a modified block-adaptive prediction-based neural network scheme for lossless data compression. A variety of neural network models from a selection of different network types, including feedforward, recurrent, and radial basis configurations are implemented with the scheme. The scheme is further expanded with combinations of popular lossless encoding algorithms. Simulation results are presented, taking characteristic features of the models, transmission issues, and practical considerations into account to determine optimized configuration, suitable training strategies, and implementation schemes. Estimations are used for comparisons of these characteristics with the existing schemes. It is also shown that the adaptations of the improvised scheme increases performance of even the classical predictors evaluated. In addition, the results obtained support that the total processing time of the two-stage scheme can, in certain cases, be faster than just using lossless encoders. Findings of the paper may be beneficial for future work, such as, in the hardware implementations of dedicated neural chips for lossless compression.     

采用改进图形变换的3D点云压缩

本文提出一种采用改进图形变换的3D点云压缩算法。所提算法首先通过改进图形变换将每个块中的所有子图连接为一个图，从源头减少直流系数个数，同时用每个块所有点的均值作为直流系数以降低直流量幅值，并对去平均的颜色值进行图形变换。考虑到量化后的交流系数的零系数占比比较大，本文采用了Run-Level的编码方法对非零的交流系数进行编码。对于直流系数，本文设计了一种预测编码方法对其进行有效编码。最后,编码完的交流系数和预测残差均采用霍夫曼编码器进行熵编码。实验结果表明所提算法相比多个现有3D点云压缩算法具有更高的压缩效率。