HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs.     

Modelling Shared Contexts in Cooperative Environments: Concept, Implementation, and Evaluation

Users who work together require adequate information about their cooperative environment: about other group members presence and activities, about shared artefacts, etc. In the CSCW literature several concepts, prototypes, and systems for providing this group awareness information have been presented. In general, they capture information from the environment, process it, and present it to the users. This paper addresses the processing aspect; in particular, we present a concept for processing awareness information by means of awareness contexts. With this concept we address the problem of contextualising event notifications enabling the presentation of notifications in the appropriate user situation. We describe a lightweight model and its integration into an event and notification infrastructure. We report on an empirical study, and draw some conclusions for the design of context-awareness for cooperative environments.     

Modelling Shared Contexts in Cooperative Environments: Concept,Implementation, and Evaluation

Users who work together require adequate information about their cooperative environment: about other group members presence and activities, about shared artefacts, etc. In the CSCW literature several concepts, prototypes, and systems for providing this group awareness information have been presented. In general, they capture information from the environment, process it, and present it to the users. This paper addresses the processing aspect; in particular, we present a concept for processing awareness information by means of awareness contexts. With this concept we address the problem of contextualising event notifications enabling the presentation of notifications in the appropriate user situation. We describe a lightweight model and its integration into an event and notification infrastructure. We report on an empirical study, and draw some conclusions for the design of context-awareness for cooperative environments.     

高动态场景的图像融合优化和实时应用

目的：由于照相机不能采集到很宽的亮度范围,过度曝光和曝光不足现象普遍存在,尤其对于高动态(HDR)场景。图像融合技术能够解决这一问题,但是由于算法大多过于复杂,难以实现高效的处理速度,只适合对静态图像做处理。本文针对图像融合算法进行了多种优化,并提出使用图像融合技术来解决视频中的曝光问题。方法：我们对图像融合算法进行测试,对块融合的方法做了有效的优化来提升算法效率,并且将优化后的算法嵌入到实时监控系统中,通过曝光控制模块与融合技术相结合,实现了实时融合的视频采集系统。结果：对高动态场景的测试表明,视频中的每一帧均能采集到整个场景中的所有信息,并且不会存在曝光问题。结论：本文针对图像融合方法提出了多种优化和加速策略,并独创性地将其应用于实时视频采集中。实验结果表明,即使是高动态场景中,也可以将几乎所有信息都保留下来,不受曝光问题的干扰。     

Haar wavelet based processor scheme for image coding with low circuit complexity

A hardware-oriented image coding processing scheme based on the Haar wavelet transform is presented. The procedure computes a variant of the Haar wavelet transform that uses only addition and subtraction operations, after that, an optimized methodology performs the selection and coding of the coefficients, tailored for it with the main aim of attaining the lowest circuit complexity hardware implementation. A selection strategy, which does not require the previous ordering of coefficients, has been used. A non-conventional coding methodology, which uses an optimized combination of techniques adapted to the various groups of coefficients, has been devised for the coding of the selected coefficients leading to a compressed representation of the image and reducing the coding problems inherent in threshold selection. The compression level reached for images of 512 × 512 pixels with 256 grey levels is just over 22:1, (0.4 bits/pixel) with a normalized mean square error, nrmse, of 2-3%, with subjective qualities which can be classified as good. The whole compression circuitry has been described and simulated at HDL level for up to 4 consecutive images, obtaining consistent results. The complete processor (excluding memory) for images of 256 × 256 pixels has been implemented using only one general-purpose low-cost FPGA chip, thus proving the design reliability and its relative simplicity.     

An immune oriented multi-agent system for biological image processing

In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.     

胎面纠偏控制系统中图像处理技术的应用

介绍了实时胎面纠偏系统的原理和结构.整个系统主要由控制器、控制对象、图像采集和图像处理单元组成,重点对图像处理单元进行研究.根据对胎面图像特点进行的分析,提出了图像处理单元的实现过程和算法分析,包括图像去噪,阈值分割,边缘检测和特征提取,计算偏移角.从提取出的胎面骨架实验结果来看,提取效果比较理想.     

A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C

The Hough Transform (HT) is a digital image processing method for the detection of shapes which has multiple uses today. A disadvantage of this method is its sequential computational complexity, particularly when a single processor is used. An optimized algorithm of HT for straight lines detection in an image is presented in this article. Optimization is realized by using a decomposition of the input image recently proposed via central processing unit (CPU), and the technique known as segment decomposition. Optimized algorithms improve execution times significantly. In this paper, the optimization is implemented in parallel using graphics processing unit (GPU) programming, allowing a reduction of total run time and achieving a performance more than 20 times better than the sequential method and up to 10 times better than the implementation recently proposed. Additionally, we introduce the concept of Performance Ratio, to emphasize the outperforming of the GPU over the CPUs.     

Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.     

Efficient algorithm for very low bit rate embedded image coding

The authors propose an embedded wavelet-based image coding algorithm that exploits both the inter- and intra-subband correlations among the wavelet coefficients. The proposed coding algorithm is based on spatial orientation trees (SOT) in which the basic unit is a block of m times n coefficients in contrast to a single coefficient in the set partitioning in hierarchical trees (SPIHT) algorithm. Each SOT has a root node (a block of m times n coefficients) in the LL-subband with the child and descendent blocks in the high frequency subbands. Thus it fuses the features of both block- and tree-based coding algorithms into a single algorithm. Performance of the proposed method is compared (in terms of rate-distortion performance) with the other state-of-the-art coding algorithms including the JPEG2000 for popular test images. Simulation results show that the proposed algorithm has a better coding efficiency over the other coders at very low bit rates. Also, compared with SPIHT it reduces the elements of the auxiliary lists, thereby reducing the memory requirements. In addition, the encoder of the proposed algorithm is significantly faster than that of the SPIHT, but with a slight increase in its decoder complexity.     

Signature file as a spatial filter for iconic image database

The problems of efficient data storage and data retrieval are important issues in the design of image database systems. A data structure called a 2-D string, which represents symbolic pictures preserving spatial knowledge, was proposed by Chang et al. It allows a natural way to construct iconic indexes for pictures. We proposed a data structure 2-D B-string to characterize the spatial knowledge embedded in images. It is powerful enough to describe images with partly overlapping or completely overlapping objects without the need of partitioning objects. When there exist a large volume of complex images in the image database, the processing time for image retrieval is tremendous. It is essential to develop efficient access methods for retrieval. In this paper, access methods, to different extents of precision, for retrieval of desired images encoded in 2-D B-strings are proposed. The signature file acting as a spatial filter of image database is based on disjoint coding and superimposed coding techniques. It provides an efficient way to retrieve images in image databases.     

Audio coding and image denoising based on the nonuniform modulated complex lapped transform

Xiong and Malvar recently introduced a nonuniform modulated complex lapped transform (NMCLT) with good time-localization and controllable frequency resolution by using an oversampled nonuniform filter bank to generate its real and the imaginary components. In this paper, we first show that oversampling in the NMCLT is not necessary in theory but a by-product of fast implementation in practice. We also point out that the amount of oversampling, which can be flexibly controlled, depends on the application. We then describe in detail the implementation of the inverse transform, which was not addressed clearly by Xiong and Malvar. We present the first applications of the NMCLT to audio coding and image denoising. A scalable audio coder has been implemented by controlling the amount of oversampling and exploiting redundancy among the NMCLT coefficients via predictive coding. Experimental results show that the audio coder reduces pre-echoes and improves the sound quality of audio clips with transient sounds. A simple denoising algorithm based on the NMCLT has also been devised to provide images with better visual quality than those obtained with wavelet-based soft thresholding.     

An attention network via pronunciation,lexicon and syntax for humor recognition

Humor is one of the most common and attractive expressions in our daily life. It is usually witty and funny. Humor recognition is an interesting but difficult task in natural language processing. Some recent works have used deep neural networks to recognize humorous text. In a different approach, we start from a new perspective based on humor linguistics, including pronunciation, lexicon, and syntax, for recognizing humor based on neural networks, in order to capture humorous incongruity and ambiguity. Specifically, we propose an attention network via pronunciation, lexicon, and syntax (ANPLS) for humor recognition. The ANPLS model contains four units, namely, the pronunciation understanding unit, the lexicon understanding unit, the syntax analysis unit, and the context understanding unit. The pronunciation understanding unit is used to extract the pronunciation-based humor features. The lexicon understanding unit is used to solve the polysemy in humor. The syntax analysis unit aims to capture the syntax information of humor. The context understanding unit is used to obtain the contextual humor features. These four units may have different levels of importance for humor recognition so that we further apply an attention mechanism to assign different weights to these four units. We conduct experiments on three popular datasets, namely, the SemEval2017 Task7 dataset, the 16000 One-Liners dataset, and the Pun of the Day dataset. The experimental results demonstrate that our model can achieve comparable or state-of-the-art performance compared with the existing models.

     

Dynamic Fractal Transform with Applications to Image Data Compression

A recent trend in computer graphics and image processing is to use Iterated Function System(IFS)to generate and describe both man-made graphics and natural images.Jacquin was the first to propose a fully automation gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper.By using this algorithm,an image can be condensely described as a fractal transform operator which is the combination of a set of reactal mappings.When the fractal transform operator is iteratedly applied to any initial image,a unique attractro(reconstructed image)can be achieved.In this paper,a dynamic fractal transform is presented which is a modification of the static transform.Instea of being fixed,the dynamic transform operator varies in each decoder iteration,thus differs from static transform operators.The new transform has advantages in improving coding efficiency and shows better convergence for the deocder.     

Fault tolerance design in JPEG 2000 image compression system

The JPEG 2000 image compression standard is designed for a broad range of data compression applications. The new standard is based on wavelet technology and layered coding in order to provide a rich feature compressed image stream. The implementations of the JPEG 2000 codec are susceptible to computer-induced soft errors. One situation requiring fault tolerance is remote-sensing satellites, where high energy particles and radiation produce single event upsets corrupting the highly susceptible data compression operations. This paper develops fault tolerance error-detecting capabilities for the major subsystems that constitute a JPEG 2000 standard. The nature of the subsystem dictates the realistic fault model where some parts have numerical error impacts whereas others are properly modeled using bit-level variables. The critical operations of subunits such as discrete wavelet transform (DWT) and quantization are protected against numerical errors. Concurrent error detection techniques are applied to accommodate the data type and numerical operations in each processing unit. On the other hand, the embedded block coding with optimal truncation (EBCOT) system and the bitstream formation unit are protected against soft-error effects using binary decision variables and cyclic redundancy check (CRC) parity values, respectively. The techniques achieve excellent error-detecting capability at only a slight increase in complexity. The design strategies have been tested using Matlab programs and simulation results are presented.     

Hierarchical multiple-SIMD architecture for image analysis

Real-time image analysis requires the use of massively parallel machines. Conventional parallel machines consist of an array of identical processors organized in either single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) configurations. Machines of this type generally only operate effectively on parts of the image analysis problem. SIMD on the low level processing and MIMD on the high level processing. In this paper we describe the Warwick Pyramid Machine, an architecture consisting of both SIMD and MIMD parts in a multiple-SIMD (MSIMD) organization which can operate effectively at all levels of the image analysis problem.     

两种常用编码方法的研究及C#实现

图像压缩是数字图像处理的一项重要技术。论文研究了基于统计特性的两种熵编码图像压缩编码方法——香农编码和哈夫曼编码,并以C#为工具,对两种编码方法进行实验及对比。实验表明,哈夫曼编码的编码效率远高于香农编码。香农编码占用的存储空间较大,单位码长表达的信息量少;哈夫曼编码节省存储空间,单位码长表达了更为丰富的信息量。     

Towards a data processing plane: An automata-based distributed dynamic data processing model

Data processing complexity, partitionability, locality and provenance play a crucial role in the effectiveness of distributed data processing. Dynamics in data processing necessitates effective modeling which allows the understanding and reasoning of the fluidity of data processing. Through virtualization, resources have become scattered, heterogeneous, and dynamic in performance and networking. In this paper, we propose a new distributed data processing model based on automata where data processing is modeled as state transformations. This approach falls within a category of declarative concurrent paradigms which are fundamentally different than imperative approaches in that communication and function order are not explicitly modeled. This allows an abstraction of concurrency and thus suited for distributed systems. Automata give us a way to formally describe data processing independent from underlying processes while also providing routing information to route data based on its current state in a P2P fashion around networks of distributed processing nodes. Through an implementation, named Pumpkin, of the model we capture the automata schema and routing table into a data processing protocol and show how globally distributed resources can be brought together in a collaborative way to form a processing plane where data objects are self-routable on the plane.