基于塔式结构的任意形状编码

在基于对象的视频编码技术中,必须对对象的形状信息进行编码.本文提出的基于位图的任意形状编码方法PSC,是利用对于二值图像进行Lazy小波变换后形成的具有层次性的金字塔结构进行形状编码的方法,该算法与CAE方法相比复杂性小,并且具有渐进性编码的特点,如果和基于小波变换的纹理编码技术联合使用,执行速度还会有很大提高.在低比特率情况下,该算法形状的率失真性能比Quad-Tree方法有很大的提高.     

三维图像的任意形状ROI小波零块压缩编码算法

感兴趣区(ROI)编码是在JPEG2000中提出的一种重要的技术,然而JPEG2000算法却无法同时支持任意形状ROI和任意提升因子.本文提出了一种基于任意形状ROI和3D提升小波零块编码的3D体数据图像压缩算法.新的算法支持ROI内外从有损到无损的编码.一种简单的任意形状无损ROI掩码(Mask)生成方法被提出.考虑到3D子带的特点,我们采用改进的3DSPECK零块算法对变换后的系数进行编码.一些其它支持任意形状ROI编码的算法也在本文中被评估,试验显示本文算法具有更好的编码性能.     

小波图像的膨胀-游程编码算法

提出了一种基于形态膨胀运算和游程编码的新型小波编码器膨胀-游程(Dilation-Run)算法。编码器根据图像小波变换后重要系数的带内聚类特性和重要系数分布的带间相似性,利用数学形态学中的膨胀运算搜索并编码各聚类中的重要系数;同时使用一种高效的游程编码技术对各聚类的种子系数,即膨胀运算起始点的位置进行编码,从而避免了小波图像中非重要系数的逐个编码。编码器算法简单,并且基于位平面实现,因此输出码流具有渐进性。实验结果表明,膨胀-游程算法的性能优于零树小波编码器SPIHT,并能与两种形态学小波编码器MRWD 和SLCCA的性能媲美。对于聚类特性显著的图像,算法的性能则优于上述形态学小波编码器。     

多维多分辨率分析和图像序列的三维子波变换压缩编码

最近有文献报道图像序列的三维子波变换压缩编码。本文讨论了多维多分辨率分析和三维子带系统完全重构的充分必要条件，我们对8帧图像序列进行三级三维子波变换，然后进行零树量化和熵编码，文中给出了不同压缩比下的信噪比。编码器可以在要求的任意压缩比下停止编码．如同图像的二维子波零树编码。     

一种利用SA-DWT的任意形状ROI编码方法

对JPEG2000的基于比例移位法进行了有效的扩展，提出了一种基于形状编码的ROI编码方法。此方法应用文中提出的VEDCC（Vertical Edge Differential Chain Coding）算法对任意形状ROI的轮廓进行编码，同时用SA—DWT算法对图像的ROI和背景区域分别进行小波变换。实验结果表明，此方法不仅能够实现多个任意形状的ROI编码，而且重构图像的任意形状ROI边缘不会因为背景区域小波系数的移位而造成图像质量下降。     

基于感兴趣区的图像编码与渐进传输

本文采用一种任意形状的整型或非整型小波变换,结合高效的SPIHT(Set Partitioning In Hierarchical Trees)图像编码算法,分别实现了任意形状感兴趣区的无损压缩和非感兴趣的有损压缩算法.另外本文还提出了两种感兴趣区优先编码策略,可以保证在极低的码率下感兴趣区的重建质量远远好于非感兴趣区的重建质量,满足用户快速、个性化的浏览需求.这些方法得到的压缩码流都具有嵌入的特点,支持渐进传输.     

嵌入式比特流混沌加密算法的研究与仿真

提出混沌与嵌入式比特流相结合的加密算法.首先,将原始图像进行二维小波变换后,转换成小波域上的小波系数,然后对小波系数进行嵌入零树小波编码.接着,使用分段线性混沌映射产生二进制混沌序列,对码流分组加密,该算法具有较高的安全性.仿真试验结果表明,该算法能够得到令人满意的结果.     

混沌神经网络在数字压缩图像加密中的应用

提出了一种混沌神经网络加密压缩数字图像的算法.首先,将原始图像进行二维小波变换后,转换成小波域上的小波系数,然后对小波系数进行嵌入零树小波编码.接着,将码流中"1"的位置取出,存入一个矩阵中,对此矩阵进行混沌神经网络加密.该加密算法具有以下特征:速度快、无失真、安全性高.仿真试验结果表明,该算法能得到令人满意的结果.     

小波变换在图像数据压缩中的应用

小波变换的图像编码方法,不仅拥有传统编码的优点,能够消除图像中的统计冗余,并且,其多分辨率的特性提供了消除非统计冗余信息的良好机制。基于离散小波变换(DWT)理论,介绍了DWT在数字图像压缩中的应用,使用零树编码实现了数字图像压缩,并同时保持原图像在各种分辨率下的精细结构,该方法对消除图像中非统计冗余信息提供了有效途径。     

一种基于EZW的ROI图像联合压缩加密算法

提出一种基于EZW编码的ROI图像联合压缩加密算法,阐述了上下文修正和判决修正的原理及实现,对所提出算法的安全性进行了评估。对算法进行仿真,结果表明相对于原始图像压缩算法,该算法的重构图像基本具有相同的压缩效率;相较于提升系数的ROI方法,该算法采用小波域图像分割,不需要对ROI形状信息进行编码,并可灵活调整ROI与背景区域(BG)的重构质量;相较于区间分裂算术编码器而言,该算法采用自适应算术编码,可获得更好的安全性;结合比特平面编码技术,使用不同密钥对ROI不同小波分辨力的系数分别加密,实现分辨力选择性加密,以满足不同应用需求。     

Texture coding on arbitrarily shaped image segments : transform methods*

The new generation of image sequence coders - the object-oriented coders-consist in describing the sequence as a set of moving objects, of arbitrary size and shape. This approach allows the development of new object-based functionalities. One of the main problems to be solved in region-based coding is the coding of the texture of the objects. As for the classical block-based coding, a linear transform can be used to eliminate the input signal redundancies. This article describes transform methods for arbitrarily shaped regions of support, classified into two categories : the shape-adaptive methods and the extrapolation methods. The main differences between these two classes are discussed and the points that need further investigation are raised.     

SAF-Nets: Shape-Adaptive Filter Networks for 3D point cloud processing

A deep learning framework for 3D point cloud processing is proposed in this work. In a point cloud, local neighborhoods have various shapes, and the semantic meaning of each point is determined within the local shape context. Thus, we propose shape-adaptive filters (SAFs), which are dynamically generated from the distributions of local points. The proposed SAFs can extract robust features against noise or outliers, by employing local shape contexts to suppress them. Also, we develop the SAF-Nets for classification and segmentation using multiple SAF layers. Extensive experimental results demonstrate that the proposed SAF-Nets significantly outperform the state-of-the-art conventional algorithms on several benchmark datasets. Moreover, it is shown that SAFs can improve scene flow estimation performance as well.     

Adaptive template filtering for signal-to-noise ratio enhancementin magnetic resonance imaging

In this paper, a local shape-adaptive template filtering is proposed for the enhancement of the signal-to-noise ratio (SNR) without the loss of resolution in magnetic resonance (MR) imaging. Unlike conventional filtering, where the template shape and coefficients are fixed, multiple templates are defined in the proposed algorithm. An optimal template is selected and optimal filtering, based on the template, is applied on a pixel-by-pixel basis. Using the proposed process, edge blurring is minimized and SNR enhancement is maximized by selecting the optimally matched template. Compared to existing two-dimensional (2-D) adaptive linear least square error (LLSE) filters or direction-adaptive recursive filters, the proposed adaptive template filter provides higher SNR and sharper edges for both MR and artificial resolution phantom images.     

Adaptive shape and texture intra refreshment schemes for improved error resilience in object-based video coding

Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop temporal error propagation when errors occur in the transmission or storage of the coded streams, since these errors may cause the decoded quality to decay very rapidly. In the context of object-based video coding, intra coding refreshment can be applied to both the shape and texture data. In this paper, novel shape and texture intra refreshment schemes are proposed which can be used by object-based video encoders, such as MPEG-4 video encoders, independently or combined. These schemes allow to adaptively determine when the shape and texture of the various video objects in a scene should be refreshed in order to maximize the decoded video quality for a certain total bit rate.     

Refreshment need metrics for improved shape and texture object-based resilient video coding

Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop error propagation when errors occur in the transmission or storage of the coded streams, which can cause the decoded quality to decay very rapidly. In the context of object-based video coding, the video encoder can apply intra coding refreshment to both the shape and the texture data. In this paper, shape refreshment need and texture refreshment need metrics are proposed which can be used by object-based video encoders, notably MPEG-4 video encoders, to determine when the shape and the texture of the various video objects in the scene should be refreshed in order to improve the decoded video quality, e.g., for a given bit rate.     

Region-based image coding with multiple algorithms

The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very limited downlink bandwidth. The authors propose a method that encodes different regions with different algorithms. The authors use three shape-adaptive image compression algorithms as the candidates. The first one is a JPEG-based algorithm, the second one is based on the object-based wavelet transform method proposed by Katata et al. (1997), and the third adopts Hilbert scanning of the regions of interest followed by one-dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that one can compare their performance on a whole rectangular image. The authors use eight Landsat TM multispectral images and another 12 small satellite single-band images as their data set. The results show that these compression algorithms have significantly different performance for different regions     

Low complexity shape-adaptive DCT for coding of arbitrarily shaped image segments

A low complexity shape-adaptive DCT transform algorithm for coding pels in arbitrarily shaped image segments is presented. The proposed algorithm is compared to the well established generalized shape-adaptive transform method introduced by Gilge et al. in terms of transform efficiency and computational complexity. Results obtained under both theoretical and experimental conditions show that the new algorithm achieves a transform efficiency close to that of the Gilge method with considerably reduced computational complexity. The proposed shape-adaptive DCT algorithm was implemented into a standard MPEG-1 coder to provide object or segment based coding of images and video with additional content-based functionality. The extended MPEG-1 object based coding scheme can handle generic input sequences and can readily provide MPEG-1 backward compatibility if no contour data is transmitted for a given video sequence. Results for INTRA coding of images indicate that the algorithm allows efficient coding over a wide range of coding parameters — thus providing means for generic coding of segmented video between very high and very low bit rates. It is further shown that some of the content-based based functionalities currently discussed in MPEG-4 can be provided efficiently using the proposed object based coding scheme.     

Image deblocking via shape-adaptive low-rank prior and sparsity-based detail enhancement

At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP) framework. More specifically, with the assumption of Gaussian quantization noise, the SALR prior is utilized to effectively separate signal from noise and preserve image edges. Compared with previous low rank priors, the SALR reconstructs a better result via shape adaptive blocks. The QC prior is also adopted to avoid over-smoothing and to enable a more accurate estimation. Finally, by extracting features of external images, the mapping matrix of sparse dictionary pairs is trained to enhance image details. Extensive experimental results demonstrate that the proposed deblocking method has superior performances in both subjective vision and objective quality.     

基于水印技术的MPEG-4形状错误隐藏

MPEG-4编码是一种基于对象内容的第二代视频编码方案,将视频根据内容分割成前景对象和背景。视频对象的信息分为形状信息、纹理信息和运动信息,而形状信息是正确解码视频对象的基础。本文就是针对MPEG-4编码标准中的I-VOP的形状信息的错误进行的错误隐藏。采用的方法有别于传统的曲线插值方法和马尔可夫域最大后验估计方法,首次采用基于鲁棒盲水印的算法,主要是将形状信息或变化作为水印信息嵌入到背景对象中。针对前人提出的方法只能修补小部分的损失,本文对于形状信息严重丢失的修复效果很好。     

Joint source-channel coding for wireless object-based video communications utilizing data hiding.

In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection.