JPEG compression history estimation for color images

We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settings-termed its JPEG compression history (CH)-are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.     

Rate-distortion estimation for fast JPEG2000 compression at low bit-rates

In JPEG2000 block coding, all coding passes are generated before rate allocation is performed among code blocks. Unwanted passes are then discarded. For low bit-rate coding, this results in a large number of coding passes being discarded. A rate-distortion estimation method that enables precompression rate-distortion optimisation to be carried out, wherein only the required passes need to be coded is presented. Experiments using the proposed technique demonstrate speed-up factors ranging from 1.17 to 1.78 at 0.0625 bpp, for JPEG2000 compression.     

Joint thresholding and quantizer selection for transform imagecoding: entropy-constrained analysis and applications to baseline JPEG

Striving to maximize baseline (Joint Photographers Expert Group-JPEG) image quality without compromising compatibility of current JPEG decoders, we develop an image-adaptive JPEG encoding algorithm that jointly optimizes quantizer selection, coefficient "thresholding", and Huffman coding within a rate-distortion (R-D) framework. Practically speaking, our algorithm unifies two previous approaches to image-adaptive JPEG encoding: R-D optimized quantizer selection and R-D optimal thresholding. Conceptually speaking, our algorithm is a logical consequence of entropy-constrained vector quantization (ECVQ) design principles in the severely constrained instance of JPEG-compatible encoding. We explore both viewpoints: the practical, to concretely derive our algorithm, and the conceptual, to justify the claim that our algorithm approaches the best performance that a JPEG encoder can achieve. This performance includes significant objective peak signal-to-noise ratio (PSNR) improvement over previous work and at high rates gives results comparable to state-of-the-art image coders. For example, coding the Lena image at 1.0 b/pixel, our JPEG encoder achieves a PSNR performance of 39.6 dB that slightly exceeds the quoted PSNR results of Shapiro's wavelet-based zero-tree coder. Using a visually based distortion metric, we can achieve noticeable subjective improvement as well. Furthermore, our algorithm may be applied to other systems that use run-length encoding, including intraframe MPEG and subband or wavelet coding.     

Image compression using wavelets and JPEG2000: a tutorial

The demand for higher and higher quality images transmitted quickly over the Internet has led to a strong need to develop better algorithms for the filtering and coding of such images. The introduction of the JPEG2000 compression standard has meant that for the first time the discrete wavelet transform (DWT) is to be used for the decomposition and reconstruction of images together with an efficient coding scheme. The use of wavelets implies the use of subband coding in which the image is iteratively decomposed into high- and low-frequency bands. Thus there is a need for filter pairs at both the analysis and synthesis stages. This paper aims in tutorial form to introduce the DWT, to illustrate its link with filters and filterbanks and to illustrate how it may be used as part of an image coding algorithm. It concludes with a look at the qualitative differences between images coded using JPEG2000 and those coded using the existing JPEG standard     

A novel deep learning framework for double JPEG compression detection of small size blocks

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.     

JPEG2000图像压缩芯片ADV-JP2000分析及应用展望

JPEG2000的主要优点ADI公司发布的图像压缩芯片ADV-JP2000是世界上首款支持JPEG2000图像压缩编码标准的芯片，该芯片市场定位在需求增长最快的消费电子产品之一 ——数码相机。JPEG2000是现在正在出台的ISO/IEC 最新静止图像压缩编码标准，与现在普遍使用的静止图像压缩编码标准JPEG(1987年制定)相比，在功能和性能上有以下几点明显的优势：更高的压缩比和图像质量JPEG2000能够提供比JPEG高一倍的压缩比，在0.1bpp时仍能提供不错的图像质量这就意味着在同样的图像质量和存储容量下，可以存储更多的图像文件。灵活的图像质量…     

Multifidelity JPEG based compression of images using variablequantisation

In image processing applications, certain regions in an image may be of greater interest to the user. For these types of applications, a multifidelity region based variable quantisation scheme is proposed. The resulting subjective visual duality is considerably higher than for comparable compression ratios employing the standard JPEG implementation     

基于JPEG标准的图像压缩DCT变换

图像DCT变换是图像压缩的一项重要技术,如何准确、快速地进行图像压缩一直是国内外研究的热点.现研究了两种二维离散余弦变换(DCT)的方法.在DCT算法结构上利用了变换的可分离性和行列的可分解性,并采用行列分解的方法将二维DCT转换为2个串行的一维DCT实现.     

IRST系统中图像实时JPEG压缩技术的实现

针对现有红外搜索跟踪系统数据量大、传输带宽高的问题,结合实际应用背景,提出了有效降低图像传输带宽的方法——JPEG图像压缩。深入研究了JPEG算法,并且详细描述了各个模块的实现方法。进而在System Generator中通过图形化语言对FPGA进行编程、仿真及实现。该方法在现有的红外系统中得到了实际应用,完成了在有限传输带宽内将系统前端红外图像传输到后端指挥中心的任务。     

发展中的静止图像压缩标准JPEG2000

从四个方面介绍JPEG2000：JEPG2000的产生背景,已有的和发展之中的静止图像压缩标准,JPEG2000的主要技术特点,JPEG2000与其它静止图像压缩标准之比较。     

JPEG图像重压缩检测综述

JPEG(Joint Photographic Experts Group,联合图像专家小组)是目前互联网上运用最为广泛的图像格式.已有的案例表明,许多篡改操作都发生在JPEG图像上,其操作基本流程是首先对JPEG文件进行解压,在空域进行篡改,篡改完成后再将篡改后的图片压缩保存为JPEG格式,这样篡改后的图片就可能会被...     

基于DSP的JPEG视频压缩系统的实现

简要介绍静止图像的压缩编码标准(JPEG)的基本原理,详细描述基于DSP的JPEG硬件压缩系统的实现和软件代码的优化.实验结果表明,按JPEG标准得到的压缩图像具有理想的压缩效果,编码耗时少,保证了嵌入式远程监控系统中图像传输的实时性.     

基于DSP的JPEG视频压缩系统的实现

简要介绍静止图像的压缩编码标准（JPEG）的基本原理,详细描述基于DSP的JPEG硬件压缩系统的实现和软件代码的优化。实验结果表明,按JPEG标准得到的压缩图像具有理想的压缩效果,编码耗时少,保证了嵌入式远程监控系统中图像传输的实时性。     

基于JPEG压缩图像的数字图像胶囊系统研究

本文研究了基于JPEG压缩图像的数字图像胶囊系统,该数字图像胶囊实现消化道图像采集与无线传送,为消化道疾病诊疗胶囊提供消化道图像信息,实现消化道疾病的诊断,并为体外磁控制提供图像导航信息。该数字图像胶囊系统采用图像压缩芯片CX93510,该芯片能够对通用CMOS图像传感器输出的图像数据进行压缩,其压缩比可配置。本系统选用低功耗CMOS图像传感器OV7740,压缩比为30,极大的减小后端无线传输的数据,降低对无线传输带宽的要求。本文完成数字图像胶囊系统原型样机设计和体外接收系统设计,为消化道诊疗胶囊提供了优异的解决方案。     

JPEG2000数据压缩的FPGA实现

高性能的数据压缩可以有效的减少数据对存储空间和通信带宽的要求,降低通信成本。为解决图像数据的高压缩性能问题,本文提出了基于JPEG2000标准的数据压缩系统的FPGA实现方案。相对于软件算法实现和其他硬件方法,采用FPGA硬件实现可降低系统复杂度提高性能。最终设计的IP核具有资源占用少,性能良好和便于扩展等优点,能够满足通信传输和照相设备等应用需求。     

Design of vector quantizer for image compression using self-organizing feature map and surface fitting

We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization.     

JPEG压缩对噪声相关性影响的修正

提出了一种修正JPEG压缩对噪声相关性大小影响的新方法。该方法应用最小二乘法估计原理求出噪声相关性大小与品质因素的函数关系,根据修正的约束条件和归一化条件推导出修正函数,利用该修正函数把各种品质因素下的噪声相关性大小修正为最高品质因素下的噪声相关性大小,使得判别阈值与品质因素无关,从而修正了JPEG压缩对噪声相关性大小的影响。实验结果表明,利用该方法产生的错误拒绝率很小,但错误接受率随着品质因素的变小而变大。     

A tutorial on modern lossy wavelet image compression: foundationsof JPEG 2000

One of the purposes of this article is to give a general audience sufficient background into the details and techniques of wavelet coding to better understand the JPEG 2000 standard. The focus is on the fundamental principles of wavelet coding and not the actual standard itself. Some of the confusing design choices made in wavelet coders are explained. There are two types of filter choices: orthogonal and biorthogonal. Orthogonal filters have the property that there are energy or norm preserving. Nevertheless, modern wavelet coders use biorthogonal filters which do not preserve energy. Reasons for these specific design choices are explained. Another purpose of this article is to compare and contrast “early” wavelet coding with “modern” wavelet coding. This article compares the techniques of the modern wavelet coders to the subband coding techniques so that the reader can appreciate how different modern wavelet coding is from early wavelet coding. It discusses basic properties of the wavelet transform which are pertinent to image compression. It builds on the background material in generic transform coding given, shows that boundary effects motivate the use of biorthogonal wavelets, and introduces the symmetric wavelet transform. Subband coding or “early” wavelet coding method is discussed followed by an explanation of the EZW coding algorithm. Other modern wavelet coders that extend the ideas found in the EZW algorithm are also described     

基于预测与JPEG2000的高光谱图像无损压缩方法

随着成像光谱仪向着高光谱分辨率、高空间分辨率方向发展,高光谱图像的数据量呈几何级数增长。由于数据传输和存储能力的限制,必须对高光谱图像进行有效压缩。首先,对高光谱图像的相关性进行了深入分析,得知其具有一定的空间相关性和极强的谱间相关性,从而具有较强的可压缩性。其次,结合JPEG2000对DPCM进行了修改,提出了基于一阶线性预测与JPEG2000相结合的无损压缩方案。最后,在软件平台上实现了该方案,并取得了较好的压缩效果。结果表明,该方案可以有效的实现高光谱图像无损压缩,验证了方案的可行性,为硬件平台上实现该方案提供了理论依据。     

基于MATLAB的DCT变换在JPEG图像压缩中的应用

介绍了JPEG图像压缩算法,并在MATLAB数学分析工具环境下从实验角度出发,较为直观地探讨了DCT在JPEG图像压缩中的应用。仿真实验表明,用MATLAB来实现离散余弦变换的图像压缩,具有方法简单、速度快、误差小的优点,大大提高了图像压缩的效率和精度。