Standard JPEG Research Articles

English

Network technologies and media services provide ubiquitous conveniences for individuals and organizations to gather and process the images in multimedia networks Image compression is the major challenge in storage and bandwidth requirements. A good strategy of image compression gives a better solution for high compression rate without much reducing the quality of the image. In present paper we proposed a simple and effective Method by filtering the image as a preprocessing step and adaptive block size in Block truncation coding at the encoding stage. The results are appealing for finding optimal block size when compare to the JPEG2000 standard.

Quantization table design revisited for image/video coding.

Quantization table design is revisited for image/video coding where soft decision quantization (SDQ) is considered. Unlike conventional approaches, where quantization table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a quantization table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a quantization table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design quantization tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG quantization table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.

Secure MQ coder: An efficient way to protect JPEG 2000 images in wireless multimedia sensor networks

This paper presents a secure MQ coder (SMQ) for efficient selective encryption of JPEG 2000 images. Being different from existing schemes where encryption overhead is proportional to the size of plain image, SMQ only selectively encrypts tiny and constant volume of data in JPEG 2000 coding regardless of image size. It is extremely fast and suitable for protecting JPEG 2000 images in wireless multimedia sensor networks (WMSNs). Theoretical analysis and experimental results show that SMQ can achieve a balance between security and efficiency, while keeping comparable compression performance and energy consumption with standard JPEG 2000 coding.

An efficient hardware implementation of MQ decoder of the JPEG2000

JPEG2000 is an international standard for still images intended to overcome the shortcomings of the existing JPEG standard. Compared to JPEG image compression techniques, JPEG2000 standard has not only better not only has better compression ratios, but it also offers some exciting features. As it’s hard to meet the real-time requirement of image compression systems by software, it is necessary to implement compression system by hardware. The MQ decoder of the JPEG2000 standard is an important bottleneck for real-time applications. In order to meet the real-time requirement we propose in this paper a novel architecture for a MQ decoder with high throughput which is comparable to that of other architectures and suitable for chip implementation. This architecture has been implemented in VHDL hardware description language and synthesized using Xilinx’s and Altera’s design flows respectively ISE 13.1 and Quartus. The implementation results show that the design operates at 439.5MHz when implemented on Virtex-6 and the estimated frame rate at this frequency is 63.24 frames per second (FPS). On Stratix III device, the design operates at 214.4MHz and the hardware cost is very low. Hardware overhead is minimized to a great extent because the structure of the probability estimation table (PET) is replaced by a small PET ROM. The memory bits used in the architecture are reduced significantly. The use of a dedicated probability estimation table decreases the internal memory.

Astronomical imagery: Considerations for a contemporary approach with JPEG2000

Abstract The new wide-field radio telescopes, such as: ASKAP, MWA, LOFAR, eVLA and SKA; will produce spectral-imaging data-cubes (SIDC) of unprecedented size—in the order of hundreds of Petabytes. Servicing such data as images to the end-user in a traditional manner and formats is likely going to encounter significant performance fallbacks. We discuss the requirements for extremely large SIDCs, and in this light we analyse the applicability of the approach taken in the JPEG2000 (ISO/IEC 15444) standards. We argue the case for the adaptation of contemporary industry standards and technologies versus the modification of legacy astronomy standards or development of new standards specific to astronomy only.

Lossy to lossless image coding based on wavelets using a complex allpass filter

Wavelet-based image coding has been adopted in the international standard JPEG 2000 for its efficiency. It is well-known that the orthogonality and symmetry of wavelets are two important properties for many applications of signal processing and image processing. Both can be simultaneously realized by the wavelet filter banks composed of a complex allpass filter, thus, it is expected to get a better coding performance than the conventional biorthogonal wavelets. This paper proposes an effective implementation of orthonormal symmetric wavelet filter banks composed of a complex allpass filter for lossy to lossless image compression. First, irreversible real-to-real wavelet transforms are realized by implementing a complex allpass filter for lossy image coding. Next, reversible integer-to-integer wavelet transforms are proposed by incorporating the rounding operation into the filtering processing to obtain an invertible complex allpass filter for lossless image coding. Finally, the coding performance of the proposed orthonormal symmetric wavelets is evaluated and compared with the D-9/7 and D-5/3 biorthogonal wavelets. It is shown from the experimental results that the proposed allpass-based orthonormal symmetric wavelets can achieve a better coding performance than the conventional D-9/7 and D-5/3 biorthogonal wavelets both in lossy and lossless coding.

Nonlinear Elastic Model for Flexible Prediction of Remotely Sensed Multitemporal Images

While an increasing number of satellite images are collected over a regular period in order to provide regular spatiotemporal information on land-use and land-cover changes, there are very few compression schemes in remotely sensed imagery that use historical data as a reference. Just as individual images can be compressed for separate transmission by taking into account their inherent spatial and spectral redundancies, the temporal redundancy between images of the same scene can also be exploited for sequential transmission. In this letter, we propose a nonlinear elastic method based on the general relationship to predict adaptively the current image from a previous reference image without any loss of information. The main feature of the developed method is to find the best prediction for each pixel brightness value individually using its own conditional probabilities to the previous image, instead of applying a single linear or nonlinear model. A codebook is generated to record the nonlinear point-to-point relationship. This temporal lossless compression is incorporated with spatial- and spectral-domain predictions, and the performances are compared with those of the JPEG2000 standard. The experimental results show an improved performance by more than 5%.

Reducing Artifacts in JPEG Decompression Via a Learned Dictionary

The JPEG compression method is among the most successful compression schemes since it readily provides good compressed results at a rather high compression ratio. However, the decompressed result of the standard JPEG decompression scheme usually contains some visible artifacts, such as blocking artifacts and Gibbs artifacts (ringing), especially when the compression ratio is rather high. In this paper, a novel artifact reducing approach for the JPEG decompression is proposed via sparse and redundant representations over a learned dictionary. Indeed, an effective two-step algorithm is developed. The first step involves dictionary learning and the second step involves the total variation regularization for decompressed images. Numerical experiments are performed to demonstrate that the proposed method outperforms the total variation and weighted total variation decompression methods in the measure of peak of signal to noise ratio, and structural similarity.

Comparison of IRIS Image Compression using JPEG 2000, SPIHT and modified SPIHT algorithm

Biometric recognition is a common and reliable way to authenticate the identity of the person. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on images of the irides of an individual's eyes. With the growing employment of the iris recognition systems and associated research to support this, the need for large databases of iris images is growing. If the required storage space is not adequate for these images, compression is an alternative. JPEG standard cannot fulfil the advanced requirements of today’s digital imagery. Digital imaging has become an integral part of the Internet and JPEG 2000 is a powerful new tool that provides power capabilities for designers and users of networked image applications. SPIHT algorithm is a very efficient technique for wavelet image compression. This is based on DWT. SPIHT is an improved and extended version of Embedded Zero tree Wavelet (EZW) coding algorithm. In this paper parameters like compression ratio, signal to noise ratio, average bit rate are obtained for JPEG 2000, SPIHT and modified SPIHT algorithms and compared.

An image compression using using discrete cosine transforms and JPEG enconder

The proposed Multiresolution decomposition through wavelets transform of fractals coded images reveal strong relationships limit the frequency content .The research is includes other extensions of the wavelet transform and fractals are wavelet transform. And our work deals with deals with the JPEG Encoder for image compression upon comparing with Discrete cosine transform (DCT) is widely used in image processing, especially for compression. Some of the applications of two-dimensional DCT involve still image compression and compression of individual video frames.The JPEG standard has been an effective first solution to the standardization of image and the performance of DWT compression with DCT compression. And JPEG 2000 is a wavelet-based standard for the compression of still digital images The wavelet transform has emerged as a cutting edge technology, within the field of image compression. And Wavelet- based coding provides substantial improvements in picture quality at higher compression ratios.and also dicuss about of the proposed DCT based JPEG Encoder and Wavelet based JPEG Encoder.

An oriented service multilayer architecture for virtual microscopy in mobile devices

Mobile devices have undergone a large transformation in the last decade, increasing their use and their possibilities. In the last two years, modern tablets have been used in many different applications because of their computational and display capacities. The possibility of using such devices in virtual microscopy highly depends on robust models that allow interacting with virtual slides. This article introduces a service oriented architecture that optimizes data interaction by a multilayer implementation of the well known JPEG2000 standard.

GPU-accelerated DXT and JPEG compression schemes for low-latency network transmissions of HD, 2K, and 4K video

Low-latency transmissions of high resolution video such as HD, 2K, or 4K over both Internet and private IP networks have found a foothold in many interactive applications, ranging from collaborative environments in science and medicine to the arts and entertainment industry. In this paper we demonstrate how the power of commodity graphics processing units can be used for efficient implementation of JPEG and DXT compression. We propose an approach to fine-grained parallelization of JPEG compression and the use of auxiliary indexes for efficient decompression, which are backward compatible with the JPEG standard. In-depth performance analysis is provided to show various aspects of the proposed parallelization including the dependency on image content and on various settings of the compression algorithm, as well as the impact of compression for interactive applications in terms of end-to-end latency. The applicability of these compression schemes in medicine and cinematography has also been evaluated using double-blind ABX tests compared with uncompressed video. We describe selected successful real-world deployment use cases based on our open-source implementation within the UltraGrid framework, such as trans-Atlantic 4K interactive video streaming during the CineGrid 2011 workshop. As discussed in the paper, the proposed approaches to parallelization provide sufficient performance even for the future generation of 8K video processing systems, currently limited by availability of camera and display hardware.

A proposal of digital rights management based on incomplete cryptography using invariant Huffman code length feature

Digital rights management (DRM) system is a promising technique to allow copyrighted content to be commercialized in digital format without the risk of revenue loss due to piracy. However, traditional DRMs are achieved with individual function modules of cryptography and watermarking. Therefore, all digital contents are temporarily disclosed in perfect condition via decryption process in the user-side risking illegal redistribution. This paper describes the basic idea of a novel DRM method composed of an incomplete cryptography using invariant Huffman code length feature and the user identification mechanism to control the quality of digital contents. The proposed incomplete cryptography consists of two processes: the incomplete encoding and the incomplete decoding. These processes are presented by randomly selecting the coefficients that belong to the same category or different category of Huffman code. In our scheme, the copyright information is embedded into the decoded content during the decoding process, and the size of digital contents are invariant during the process. Experimental results with simulation confirmed that the modified codes are compatible with standard JPEG format, and revealed the proposed method to be suitable for DRM in the network distribution system.

ADAPTIVE TCHEBICHEF MOMENT TRANSFORM IMAGE COMPRESSION USING PSYCHOVISUAL MODEL

An extension of the standard JPEG image compression known as JPEG-3 allows rescaling of the quantization matrix to achieve a certain image outp ut quality. Recently, Tchebichef Moment Transform (TMT) has been introduced in the field of image compression. TMT has been shown to perform better than the standard JPEG image compression. This study presents an adaptive TMT image compression. This task is obtained by generating custom quantization tables f or low, medium and high image output quality levels based on a psychovisual model. A psychovisual model is developed to approximate visual threshold on Tchebichef moment from image reconstruction error. The contribution of each moment will be investigated and analy zed in a quantitative experiment. The sensitivity of TM T basis functions can be measured by evaluating the ir contributions to image reconstruction for each mome nt order. The psychovisual threshold model allows a developer to design several custom TMT quantization tables for a user to choose from according to his or her target output preference. Consequently, these quant ization tables produce lower average bit length of Huffman code while still retaining higher image quality tha n the extended JPEG scaling scheme.

Applied Minimized Matrix Size Algorithm on the Transformed Images by DCT and DWT used for Image Compression

paper a simple image compression scheme is proposed, it is based on using two transformation techniques in the frequency domain of DWT and DCT to decompose the image signal and then using Minimize-Matrix-Size algorithm to efficiently encode the compress information that reduces high-frequency matrix size, and makes it easy to be compressed by arithmetic coding. The test results of the suggested method are promising compared with JPEG and JPEG2000 international standards techniques.

Image Compression by Embedding Five Modulus Method into JPEG

The standard JPEG format is almost the optimum format in image compression. The compression ratio in JPEG sometimes reaches 30:1. The compression ratio of JPEG could be increased by embedding the Five Modulus Method (FMM) into the JPEG algorithm. The novel algorithm gives twice the time as the standard JPEG algorithm or more. The novel algorithm was called FJPEG (Five-JPEG). The quality of the reconstructed image after compression is approximately approaches the JPEG. Standard test images have been used to support and implement the suggested idea in this paper and the error metrics have been computed and compared with JPEG.

WDCT-based Image Coding Algorithm with Down-Sampling and APDCT Interpolation

Abstract The DCT-based JPEG standard is the most commonly used lossy compression algorithm for still continuous-tone images due to its high effectiveness and low computational complexity. However, the visually disturbing blocking artifacts generate when image is compressed at low bit rates. With the frequency warping, the warped discrete cosine transform (WDCT) makes the signal energy distribution more suitable for image coding. In order to improve the coding performance at low bit rates, this paper presents an image coding frame based on down-sampling and all phase DCT (APDCT) interpolation. Experimental results show that, no matter for PSNR or subjective result, the proposed method obviously improves the performance of image coding at low bit rates, and reduces the blocking artifacts significantly.

Fast block prediction-based coding of compound images by exploiting edge orientation

Compound images are a combination of text, graphics and natural images. This paper presents a new fast block prediction algorithm for coding of text/graphics parts which exploits the direction of edges in the text/graphics blocks. The proposed scheme classifies the text/graphics blocks into four kinds of blocks using edge characteristics of transformed coefficients of blocks. This edge–based block classification improves the speed of intra–block prediction mode decisions significantly. Experimental results show that the proposed method improves the value of PSNR more significantly than standard JPEG, JPEG–2000 and H.264/AVC–I, while keeping competitive compression ratio and visually lossless quality of text information.

Image Steganography Based on Wavelet Families

Image Steganography Based on Wavelet Families Wavelet transforms are considered to be an ideal domain for image compression and transmission. The new generation still image compression standard JPEG2000 uses the bi-orthogonal CDF 5/3 wavelet (also called the CDF (2, 2) wavelet) for lossless compression and a CDF 9/7 wavelet for lossy compression. There are several known wavelet families such as Daubechies, Coiflet, Symlet, CDF, etc. The problem of selecting a suitable wavelet for signal and image processing has always challenged the researchers. The conventional wavelet filters often have floating point coefficients and couldn’t realize the lossless reconstruction. The second generation wavelet transforms are based on lifting scheme and they map integers to integers. Thus they realize the lossless compression of image data with minimal memory usage and low computational complexity.

Rate pre-allocated compression for mapping image based on wavelet and rate-distortion theory

In order to solve the problem that the general image compression algorithm and the running speed based on single DSP or PC cannot satisfied the demand for high-resolution mapping real-time image compression, this paper proposed a low-complexity rata pre-allocated algorithm and an implement mode. Firstly, the multi-level integer wavelet applied to image. Secondly, depend on the principle that various sub-band important to image reconstruction, and the optimal rate-distortion theory, pre-allocated the actual contribution rate in the coding for the each sub-band. Lastly, when each sub-band has been coded, the coding have been truncated in real-time according to the pre-allocated rate, so the bit rate size of the compressed image has been controlled precisely. Test results show that this paper proposed algorithm which has more features than the other classical standard compression algorithm. The features include less calculation, less usage of storage space, and simple structure of the bit rate stream. The algorithm with low complexity but the same compression performance as the JPEG2000 standard is significantly better than SPIHT and other algorithms. Finally, the algorithm proposed in this paper has been realizes in real-time by many DSP and FPGA.