Hybrid fractal image coding with quadtree-based progressive structure

A progressive structure which takes the quadtree depth into consideration is proposed for fractal image coding. Simulation results show that its image quality at different received data rates is better than that without considering the quadtree level. Then, a hybrid fractal image coding scheme based on traditional and no-search fractal image coding with the proposed progressive structure is suggested. The image quality and compression ratio can be controlled by a threshold, which makes it downward compatible to the no-search fractal image coding. Experimental results justify that the progressive performance of the proposed scheme is better than that of traditional fractal image coding.     

Progressive source coding for a power constrained Gaussian channel

We consider the progressive transmission of a lossy source across a power constrained Gaussian channel using binary phase-shift keying modulation. Under the theoretical assumptions of infinite bandwidth, arbitrarily complex channel coding, and lossless transmission, we derive the optimal channel code rate and the optimal energy allocation per transmitted bit. Under the practical assumptions of a low complexity class of algebraic channel codes and progressive image coding, we numerically optimize the choice of channel code rate and the energy per bit allocation. This model provides an additional degree of freedom with respect to previously proposed schemes, and can achieve a higher performance for sources such as images. It also allows one to control bandwidth expansion or reduction     

Robust image communication over wireless channels

Typical image compression algorithms produce data streams which require a very reliable communication-they are not designed for transmission in an environment in which data may be lost or delayed, as provided by current and next-generation wireless communication networks. Compression and transmission provisions that avoid catastrophic failure caused by lost, delayed, or errant packets are therefore imperative in order to provide reliable visual communication over such systems. This robustness is obtained by modifying the source coding and/or adding channel coding. This article presents an overview of both lossy and lossless source coding techniques and combined source/channel techniques providing robustness, presenting examples of successful techniques     

Low entropy image pyramids for efficient lossless coding

An efficient image source coding technique gives good compression performance at low computational complexity. This research introduces an efficient coding technique, based on pyramid coding, that involves transforming an image into an equivalent lower entropy form prior to lossless coding. The proposed method is also a multiresolution technique that facilitates progressive image transmission.     

A GAN based method for multiple prohibited items synthesis of X-ray security image

Detecting prohibited item based on convolutional neural networks(CNNs) is of great significance to ensure public safety. However, the natural occurrence of such prohibited items is a small-probability event, collecting enough datasets to support CNN training is a big challenge. In this paper, we propose a new method for synthesizing X-ray security image with multiple prohibited items from semantic label images basing on Generative Adversarial Networks(GANs). Theoretically, we can use it to synthesize as many X-ray images as needed. A new generator architecture with Res 2 Net is presented, which is more effective in learning multi-scale features of different prohibited items images. This method is extended by establishing the semantic label library which contains 14 000 images. So we totally synthesize 14 000 Xray security images. The experimental results show the super performance(Fréchet Inception Distance(FID) score of 30.55). And we achieve 0.825 of mean average precision(m AP) with Single Shot Multi Box Detector(SSD) for object detection, demonstrating the effectiveness of our approach.     

基于整数小波变换的有损与无损嵌入式图像压缩

提出了基于整数小波变换的嵌入式图像编码方案，能够进行有损和无损两种模式的图像压缩，在非常低的比特率时能够获得较高质量的重构图像。该方法适合图像的渐进传输，它是使用扩增的零树信号和位面编码的合成技术，最后对产生的符号流进行算术编码。文中给出了该方法与有损和无损压缩标准的性能比较。     

A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate–distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512×512 image, as compared with that of the SPIHT algorithm.     

Making Progressive Transmission Adaptive

Progressive transmission hierarchically reorganizes the information in an image with the aim of increasing transmission efficiency on a low bandwidth channel. We have developed methods for adaptively choosing initial and successive approximations to be sent when the end user is a human telebrowsing through a database to find particular desired images. Because of progressive transmission overhead, it is desirable to send in one step the approximation which is just good enough to allow a user to make a positive decision (e.g., want/don't want). During past and present sessions of telebrowsing, positive response times are recorded and used to estimate the actual positive response time probability mass function. This estimate is used with a previously calculated overhead function to choose the best initial and successive approximations. We discuss the application of two-channel coding to progressive transmission and conclude that little is gained by further coding of the initial lowresolution approximation. However, the interpretation of these lowresolution images is enhanced if the displayed size is a function of image resolution. We present a method for determination of the best displayed image size which is based on the modulation transfer function of the human visual system.     

基于DCT变换的渐进式图像编码方法

本文将DCT变换与SPECK算法相结合,提出了一种基于DCT变换的渐进式图像编码方法.该方法充分利用了DCT变换实现简单的特点,还能充分发挥零树编码的优势实现对图像的高效压缩.输出码流具有嵌入的特性,便于码率控制,具有一定的抗误码能力.同时计算复杂度低,比较适合于低复杂度的图像编码与传输.     

基于小波变换的彩色图像压缩编码在分级传输中的应用

本文研究了彩色图像的压缩编码在分级传输中的应用。选择双正交小波,对彩色图介的Y、I和Q分量分别进行小波分解。然后根据变换域的数据特性并结合视觉系统(HVS)特性,提出了一种合适的压缩编码方案,并给出了图像分级传输的效果。实验结果表明,这种压缩编码适用于图像的分级传输。     

Importance prioritisation in JPEG 2000 for improved interpretability

Importance prioritised coding is an image coding principle aimed at improving the interpretability versus bit-rate performance of image coding systems. It is important in surveillance where image formats are large and transmission over bandlimited channels can take considerable time even for compressed bit-streams. It is also useful in content-based retrieval and browsing applications, where the number of images viewed tend to be large and faster interpretability would imply faster rejection of unwanted partially decompressed images. An importance prioritised image coder incorporated within the JPEG 2000 framework, called IMP-J2K, is presented, to prioritise image contents according to a metric based on its ‘importance’. The performance of IMP-J2K is also quantitatively assessed using the objective peak signal-to-noise ratio quality and subjective national imagery interpretability rating scale.     

Dilation-run wavelet image coding

The run-length coding and the morphological representation are two classical schemes for wavelet image coding. The run-length coders have the advantage of simplicity by recording the lengths of zero-runs between significant wavelet coefficients but at the expense of yielding an inferior rate-distortion performance. The morphology-based coders, on the other hand, utilize the morphological dilation operation to delineate the clusters of significant coefficients for improving coding performance. In this paper, a novel dilation-run image coding algorithm is developed by taking the advantages of both schemes, in which the clustered significant coefficients are extracted by using the morphological dilation operation and the insignificant coefficients between the extracted clusters are coded by using the run-length coding method. The proposed dilation-run image coder is implemented in the framework of bitplane coding for producing embedded bitstreams. Compared with several state-of-the-art wavelet image coding methods, the proposed dilation-run image coding method achieves comparable rate-distortion coding performance, especially more attractive for fingerprint type of imageries.     

Irregular Segmented Region Compression Coding Based on Pulse Coupled Neural Network

An irregular segmented region coding algorithm based on pulse coupled neural network（PCNN） is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.     

新一代静止图像编码系统-JPEG2000

JPEG2000是最新制定的静止图像编码国际标准，JPEG2000不仅提供的率失真性能和主观图像质量优于原JPEG标准，而且在支持渐进图像传输、感兴趣区图像编码和抗误码性能上也优于传统的JPEG标准和其它编码方法。本文对JPEG2000系统的结构、特性以及其编码算法进行了分析，并给出JPEG2000与原JPEG标准的性能比较。     

Hybrid fractal zerotree wavelet image coding

In this paper, a hybrid fractal zerotree wavelet (FZW) image coding algorithm is proposed. The algorithm couples a zerotree-based encoder, such as the embedded zerotree wavelet (EZW) coder or set partitioning in hierarchical trees, and a fractal image coder; this coupling is done in the wavelet domain. Based on perceptually-weighted distortion-rate calculations, a fractal method is adaptively applied to the parts of an image that can be encoded more efficiently relative to an EZW coder at a given rate. In addition to improving compression performance, the proposed algorithm also allows one to impose desirable properties from each type of image coder, such as progressive transmission, the zerotree structure, and range-domain block decoding.     

视频图像中端至端失真估算的研究

视频图像数据量大、压缩码流抗误性低,而现有通信网络的承载能力有限、传输性能不可靠,往往造成解码重建视频图像质量致命性地降低。有效控制信源与信道编码,使端至端失真最小是提高解码重建视频图像质量的关键,而精确的失真估算模型是获得最小端至端失真的前提。对几种典型的端至端失真估算模型进行了简要分析,指出了这些模型中待解决的关键问题并展望了端至端失真估算模型良好的发展前景。     

低密度校验码及其在图像传输中的应用

低密度校验(Low-Density Parity-Check,LDPC)码是一种基于图和迭代译码的信道编码方案,性能非常接近Shannon极限且实现复杂度低,具有很强的纠错抗干扰能力。该文深入研究了LDPC码的编码和译码基本原理,并将其应用于移动衰落信道图像的传输中,仿真结果表明LDPC码能为图像传输带来显著的性能提高,且系统复杂度低,译码时延短。     

Error resilient pre/post-filtering for DCT-based block coding systems.

Block coding based on the discrete cosine transform (DCT) is very popular in image and video compression. Pre/post-filtering can be attached to a DCT-based block coding system to improve coding efficiency as well as to mitigate blocking artifacts. Previously designed pre/post-filters are optimized to maximize coding efficiency solely. For image and video communication over unreliable channels, those pre/post-filters are sensitive to transmission errors. This paper addresses the problem of designing pre/post-filters which are more error resilient. Reconstruction performance is measured by how low the average reconstruction error is, and how uniformly the reconstruction error is distributed. A family of pre/post-filters is designed to provide desired tradeoffs between coding efficiency and robustness to transmission errors. Experiments show that these filtering operators can achieve superior reconstruction performance without sacrificing much coding performance.     

A Subjectively Adapted Image Communication System

An interactive image communication system is described transmitting image information stored in a central database over low-bitrate channels. To shorten the transmission time, a data compression technique is applied in combination with a progressive image transmission procedure. Furthermore, human interaction is implemented in order to select specific areas for picture buildup or to reject additional image sharpening completely. The mean transmission time for each picture is essentially reduced if the transmission parameters of the investigated system are adapted to the visual threshold performance of the human eye. The adaptation is realized by means of the given classification algorithm. For a set of portrait pictures, more than 95 percent of the transmission time can be gained by this adaptation in comparison with PCM transmission.     

Spatially constrained sparse coding scheme for natural scene categorization

Coding and pooling, the major two sequential procedures in sparse coding based scene categorization systems, have drawn much attention in recent years. Yet improvements have been made for coding or pooling separately, this paper proposes a spatially constrained scheme for sparse coding on both steps. Specifically, we employ the m-nearest neighbors of a local feature in the image space to improve the consistency of coding. The benefit is that similar image features will be encoded with similar codewords, which reduced the stochasticity of a conventional coding strategy. We also show that the Viola–Jones algorithm, which is well-known in face detection, can be tailored to learning receptive fields, embedding the spatially constrained information on the pooling step. Extensive experiments on the UIUC sport event, 15 natural scenes and the Caltech 101 database suggests that scene categorization performance of several popular algorithms can be ubiquitously improved by incorporating the proposed two spatially constrained sparse coding scheme.