基于分段式拟合的低比特率图像压缩编码方法

为了提高图像在低比特率条件下的解码质量和视觉效果,该文提出一种基于稀疏分解的低比特率图像压缩编码新方法。利用二维不可分离、具有各向异性尺度的墨西哥草帽小波作为生成函数,这种函数构建的冗余字典能够有效捕获图像边缘轮廓特征。为降低原子投影系数的冗余度和减小其编码量,对图像稀疏分解投影的系数采用分段式拟合。获得的图像压缩码流具有渐进特性的,满足现代无线通信对可伸缩码流的要求。实验结果表明,在低比特率下,该文方法与JPEG2000方法和通常的稀疏分解编码法相比,有更高的峰值信噪比;解压图像无振铃效应,主观效果好。     

Low bit-rate cloud-based image coding in the wavelet transform domain

The rapid growth of image resources on the Internet makes it possible to find some highly correlated images on some Web sites when people plan to transmit an image over the Internet. This study proposes a low bit-rate cloud-based image coding scheme, which utilizes cloud resources to implement image coding. Multiple- discrete wavelet transform was adopted to decompose the input image into a low-frequency sub-band and several high-frequency sub-bands. The low-frequency sub-band image was used to retrieve highly correlated images (HCOIs) in the cloud. The highly correlated regions in the HCOIs were used to reconstruct the high-frequency sub-bands at the decoder to save bits. The final reconstructed image was generated using multiple inverse wavelet transform from a decompressed low-frequency sub-band and reconstructed high-frequency sub-bands. The experimental results showed that the coding scheme performed well, especially at low bit rates. The peak signal-to-noise ratio of the reconstructed image can gain up to 7 and 1.69 dB over JPEG and JPEG2000 under the same compression ratio, respectively. By utilizing the cloud resources, our coding scheme showed an obvious advantage in terms of visual quality. The details in the image can be well reconstructed compared with both JPEG, JPEG2000, and intracoding of HEVC.     

A deblocking technique for block-transform compressed image usingwavelet transform modulus maxima

We introduce a deblocking algorithm for Joint Photographic Experts Group (JPEG) decoded images using the wavelet transform modulus maxima (WTMM) representation. Under the WTMM representation, we can characterize the blocking effect of a JPEG decoded image as: (1) small modulus maxima at block boundaries over smooth regions; (2) noise or irregular structures near strong edges; and (3) corrupted edges across block boundaries. The WTMM representation not only provides characterization of the blocking effect, but also enables simple and local operations to reduce the adverse effect due to this problem. The proposed algorithm first performs a segmentation on a JPEG decoded image to identify the texture regions by noting that their WTMM have small variation in regularity. We do not process the modulus maxima of these regions, to avoid the image texture being "oversmoothed" by the algorithm. Then, the singularities in the remaining regions of the blocky image and the small modulus maxima at block boundaries are removed. We link up the corrupted edges, and regularize the phase of modulus maxima as well as the magnitude of strong edges. Finally, the image is reconstructed using the projection onto convex set (POCS) technique on the processed WTMM of that JPEG decoded image. This simple algorithm improves the quality of a JPEG decoded image in the senses of the signal-to-noise ratio (SNR) as well as the visual quality. We also compare the performance of our algorithm to the previous approaches, such as CLS and POCS methods. The most remarkable advantage of the WTMM deblocking algorithm is that we can directly process the edges and texture of an image using its WTMM representation.     

Two-Dimensional Adaptive Decimation and Interpolation Scheme for Low Bit-Rate Image Coding

A novel image compression scheme based on two-dimensional adaptive decimation is reported in this paper. In this approach, images are encoded with adaptive sampling along the horizontal and vertical directions, and decoded with an edge prediction interpolation algorithm. The method is capable of maintaining reasonable coding fidelity at low bit-rate with good visual quality. As only a small amount of computation is required in the encoding and decoding processes, the compression scheme can be implemented for real time operation with simple hardware and a small amount of memory storage. The proposed scheme had been applied in encoding images at bit-rates between 0.2 and 0.33 bpp and the results are encouraging.     

JPEG2000实时截断码率控制新算法及其VLSI结构设计

提出一种实时编码实时截断的码率控制算法.它根据已分解的小波子带内码块有效位平面数来预测未分解的小波子带内码块有效位平面数,并根据编码通道数和小波/量化权系数为当前编码码块分配码率.并提出一种JPEG2000编码实时截断,两级码率控制的编码体系结构.第一级采用本文提出的算法实时截断码流和编码通道.第二级在低码率下采用JPEG2000标准的PCRD优化算法搜索精确的分层截断点.在最优分层截断之前多数码流和编码通道被预先截断,存储器损耗小,实时性高.低码率下,图像质量跟JPEG2000标准一致.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

基于ADV202的遥感图像实时压缩系统设计

提出了以JPEG2000图像压缩标准作为压缩算法,采用JPEG2000专用编解码芯片ADV202来实现遥感图像实时压缩系统的方案.该方案可以满足遥感图像压缩系统对实时性、低失真以及高压缩比的要求.测试结果表明,该系统工作稳定可靠,能够满足遥感图像压缩系统实时性的要求,图像压缩效果令人满意.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

基于JPEG2000的遥感图像感兴趣区域编码新算法及其VLSI设计

为解决空间遥感图像数据量及信道带宽之间的矛盾,该文提出一种基于JPEG2000的感兴趣区域(Region Of Interest, ROI)编码算法。主流的JPEG2000 ROI编码算法难以兼顾ROI质量和系统计算量,且在低码率编码时有完全丢失背景的隐患。该算法通过精确控制各子带中背景系数的精度,使ROI分配到更多码流。并引入了人眼视觉特性,使较少的背景码流产生尽量好的视觉效果。另外,根据该算法提出了针对矩形ROI的超大规模集成电路(VLSI)设计,此设计经过简单调整,亦可适用于主流的ROI编码算法。测试结果表明,该算法在ROI质量和重建图像视觉效果上均表现优异,且支持任意形状ROI编码,兼容JPEG2000协议。该VLSI设计仅使JPEG2000系统运行时间增加一个周期,具有极高的吞吐率,可满足实时处理要求。     

GA-based DCT quantisation table design procedure for medical images

Medical images are widely used in the diagnosis of diseases. These imaging modalities include computerised tomography (CT), magnetic resonance imaging (MRI), ultrasonic (US) imaging, X-radiographs, etc. However, medical images have large storage requirements when high resolution is demanded; therefore, they need to be compressed to reduce the data size so as to achieve a low bit rate for transmission or storage, while maintaining image information. The Joint Photographic Experts Group (JPEG) developed an image compression tool that is one of the most widely used products for image compression. One of the factors influencing the performance of JPEG compression is the quantisation table. The bit rate and the decoded quality are determined simultaneously by the quantisation table, and therefore, the table has a strong influence on the whole compression performance. The author aims to provide a design procedure to seek sets of better quantisation parameters to raise the compression performance to achieve a lower bit rate while preserving high decoded quality. A genetic algorithm (GA) was employed to promote higher compression performance for medical images. The goal was to develop a design procedure to find quantisation tables that contribute to better compression efficiency in terms of bit rate and decoded quality. Simulations were carried out on different kinds of medical images. Resulting experimental data demonstrate that the GA-based search procedures can generate better performance than JPEG 2000 and JPEG even though the training images have different features. Additionally, if existing published quantisation tables are put into the crossover pool in the proposed GA-based system, it can improve the performance by yielding better quantisation tables.     

Lossless compression of HDR color filter array image for the digital camera pipeline

This paper introduces a lossless color filter array (CFA) image compression scheme capable of handling high dynamic range (HDR) representation. The proposed pipeline consists of a series of pre-processing operations followed by a JPEG XR encoding module. A deinterleaving step separates the CFA image to sub-images of a single color channel, and each sub-image is processed by a proposed weighted template matching prediction. The utilized JPEG XR codec allows the compression of HDR data at low computational cost. Extensive experimentation is performed using sample test HDR images to validate performance and the proposed pipeline outperforms existing lossless CFA compression solutions in terms of compression efficiency.     

Selective Error Detection for Error-Resilient Wavelet-Based Image Coding

This paper introduces the concept of a similarity check function for error-resilient multimedia data transmission. The proposed similarity check function provides information about the effects of corrupted data on the quality of the reconstructed image. The degree of data corruption is measured by the similarity check function at the receiver, without explicit knowledge of the original source data. The design of a perceptual similarity check function is presented for wavelet-based coders such as the JPEG2000 standard, and used with a proposed ldquoprogressive similarity-based ARQrdquo (ProS-ARQ) scheme to significantly decrease the retransmission rate of corrupted data while maintaining very good visual quality of images transmitted over noisy channels. Simulation results with JPEG2000-coded images transmitted over the binary symmetric channel, show that the proposed ProS-ARQ scheme significantly reduces the number of retransmissions as compared to conventional ARQ-based schemes. The presented results also show that, for the same number of retransmitted data packets, the proposed ProS-ARQ scheme can achieve significantly higher PSNR and better visual quality as compared to the selective-repeat ARQ scheme.     

Fractal coding of subbands with an oriented partition

We propose a new image compression scheme based on fractal coding of the coefficients of a wavelet transform, in order to take into account the self-similarity observed in each subband. The original image is first decomposed into subbands containing information in different spatial directions and at different scales, using an orthogonal wavelet-generated filter bank. Subbands are encoded using local iterated function systems (LIFS), with range and domain blocks presenting horizontal or vertical directionalities. Their sizes are defined according to the correlation lengths and resolution of each subband. The edge degradation and the blocking effects encountered at low bit-rates using conventional LIFS algorithm are reduced with this approach. The computation complexity is also greatly decreased by a 12:1 factor in comparison to fractal coding of the full resolution image. The proposed method is applied to standard test images. The comparison with other fractal coding approaches and with JPEG shows an important increase in terms of PPSNR/bit-rate. Especially for images presenting a privileged directionality, the use of adaptive partitions results in about 3 dB improvement in PPSNR. We also discuss the distorsion versus rate improvement obtained on high-frequency subbands when fractal coding instead of pyramidal vector quantization is used. Our approach achieves a real gain in PPSNR for low bit-rates between 0.3 and 1.2 bpp.     

一种基于JPEG2000的自适应联合信源信道编码方法

针对JPEG2000图像在噪声条件未知的信道上有效和可靠传输问题,本文提出了一种基于反馈的自适应联合信源信道编码方案.首先采用基于子空间的信噪比估计方法实时估计信道的信噪比SNR,进而求得信道的误码率,解决了对信道的自适应问题.其次,充分利用了JPEG2000的码流质量分级特点,建立了实用的目标函数,从而根据数据包对重建图像贡献的不同,实施不同的保护,以低复杂度的运算实现了码率优化分配的自适应过程.仿真结果表明,和现有的联合信源信道编码算法相比,这种自适应联合优化编码方法可以取得较好的效果.     

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 High-Capacity Steganography Scheme for JPEG2000 Baseline System

Hiding capacity is very important for efficient covert communications. For JPEG2000 compressed images, it is necessary to enlarge the hiding capacity because the available redundancy is very limited. In addition, the bitstream truncation makes it difficult to hide information. In this paper, a high-capacity steganography scheme is proposed for the JPEG2000 baseline system, which uses bit-plane encoding procedure twice to solve the problem due to bitstream truncation. Moreover, embedding points and their intensity are determined in a well defined quantitative manner via redundancy evaluation to increase hiding capacity. The redundancy is measured by bit, which is different from conventional methods which adjust the embedding intensity by multiplying a visual masking factor. High volumetric data is embedded into bit-planes as low as possible to keep message integrality, but at the cost of an extra bit-plane encoding procedure and slightly changed compression ratio. The proposed method can be easily integrated into the JPEG2000 image coder, and the produced stego-bitstream can be decoded normally. Simulation shows that the proposed method is feasible, effective, and secure.      

Low-rate and flexible image coding with redundant representations.

New breakthroughs in image coding possibly lie in signal decomposition through nonseparable basis functions that can efficiently capture edge characteristics, present in natural images. The work proposed in this paper provides an adaptive way of representing images as a sum of two-dimensional features. It presents a low bit-rate image coding method based on a matching pursuit (MP) expansion, over a dictionary built on anisotropic refinement and rotation of contour-like atoms. This method is shown to provide, at low bit rates, results comparable to the state of the art in image compression, represented here by JPEG2000 and SPIHT, with generally a better visual quality in the MP scheme. The coding artifacts are less annoying than the ringing introduced by wavelets at very low bit rate, due to the smoothing performed by the basis functions used in the MP algorithm. In addition to good compression performances at low bit rates, the new coder has the advantage of producing highly flexible streams. They can easily be decoded at any spatial resolution, different from the original image, and the bitstream can be truncated at any point to match diverse bandwidth requirements. The spatial adaptivity is shown to be more flexible and less complex than transcoding operations generally applied to state of the art codec bitstreams. Due to both its ability for capturing the most important parts of multidimensional signals, and a flexible stream structure, the image coder proposed in this paper represents an interesting solution for low to medium rate image coding in visual communication applications.     

Medical image compression by discrete cosine transform spectralsimilarity strategy

Due to bandwidth and storage limitations, medical images must be compressed before transmission and storage. However, the compression reduces the image fidelity, especially when the images are compressed at low bit rates. The reconstructed images suffer from blocking artifacts and the image quality is severely degraded under high compression ratios. In this paper, we present a strategy to increase the compression ratio with low computational burden and excellent decoded quality. We regard the discrete cosine transform as a bandpass filter to decompose a sub-block into equal-sized bands. After a band-gathering operation, a high similarity property among the bands is found. By utilizing the similarity property, the bit rate of compression can be greatly reduced. Meanwhile, the characteristics of the original image are not sacrificed. Thus, it can avoid the misdiagnosis of diseases. Simulations were carried out on different kinds of medical images to demonstrate that the proposed method achieves better performance when compared to other existing transform coding schemes, such as JPEG, in terms of bit rate and quality. For the case of angiogram images, the peak signal-to-noise-ratio gain is 13.5 dB at the same bit rate of 0.15 bits per pixel when compared to the JPEG compression. As for the other kinds of medical images, their benefits are not so obvious as for angiogram images; however, the gains for them are still 4-8 dB at high compression ratios. Two doctors were invited to verify the decoded image quality; the diagnoses of all the test images were correct when the compression ratios were below 20     

Progressive Image Transmission Using LOT with Two-Channel Conjugate VQ

A progressive image transmission (PIT) scheme based on the classified two-channel conjugate VQ (TCCVQ) technique in the lapped orthogonal transform (LOT) domain is proposed. Conventional block transform coding of images using DCT produces in general undesirable block-artifacts at low bit rates. In this paper, image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics to improve the reconstructed image quality by adaptive bit allocation. The subvectors are quantized using the two-channel conjugate VQ technique which has less computational complexity and less storage memory, and is more robust against channel errors. The vector quantized subvectors are transmitted and decoded in stages so that an image is progressively reconstructed, i.e., initially a crude version followed by quality build up in successive stages as the occasion demands in interactive visual communications. Coding tests using computer simulations show that the LOT/TCCVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using conventional classified VQ in both the DCT and LOT domains.     

Implementation of TiOISSS with meaningful shadows and with an additional authentication image

Visual Cryptography Scheme (VCS) is a cryptographic technique for protecting secret images. The advantage of using VCS is that decoding can be done without use of any computations. Nevertheless, the reconstructed image has poor visual quality. Therefore, Two in One Image Secret Sharing Scheme (TiOISSS) was proposed which takes the advantage of VCS and provides good quality decoded images. However, the existing TiOISSS has security limitations as it is implemented only for noisy shadows. Hence, in this paper, modified TiOISSS is proposed and implemented for meaningful shadows. To enhance the security of the shares and prevent fake shares that may be introduced by hackers, an authentication image is shared along with the secret image. The quality of the reconstructed image is improved by using adaptive halftoning technique. Experimental results demonstrate the improved security and quality by the proposed scheme.