Performance analysis of JPEG algorithm

Aim: High quality of all image elements must be maintained for the purpose of telepathology. The aim of this study was to compress the images of electrophoresis and isoelectric focusing samples using JPEG (Joint Photographic Expert Group) and JPEG2000 algorithms, and to assess the quality of the compressed images before and after electronic transmission. Methods: Scanograms of serum protein electrophoresis samples of a patient with Zagreb albumin and albumin samples for isoelectric focusing of a patient with Krapina albumin were selected for the study together with a photographed scanogram of isoelectric focusing. Each image was compressed at eight compression rates (from 3.00 bpp (bit per pixel) to 0.1 bpp) using JPEG and JPEG2000 compression algorithms. All images (N = 51), both compressed and uncompressed, we retransmitted by email for assessment to eight medical biochemists: six from Croatia, one from Italy and one from Denmark. Image quality was also assessed by objective measures, i.e. compared to the quality of PSNR (Peak-Signal-to-Noise-Ratio), SNR (Signal-to-Noise-Ratio), OQF (Optimized Quality Factor) and MSE (Mean Squared Error) images. Results: All images compressed using the JPEG2000 algorithm were subjectively rated as excellent. Contrarily, images compressed using JPEG at 0.1 bpp were rated as completely useless, those at 0.2 bpp as moderately blurred, and those at 0.3-3.00 as excellent. At JPEG compression at 0.3 bpp, PSNR and SNR values corresponded to PSNR and SNR values obtained by JPEG2000 compression at 0.1 bpp.

This paper proposes image quality measures based on multifractality preservation. Image quality measures play critical roles in designing and evaluating image processing schemes and performance, especially in which the resulting images deviate from the original ones. Examples of such schemes include image denoising and lossy compression. Traditional quality or distortion measures have been based on mean square error (MSE) measure or its derivatives, e.g., signal to noise ratio (SNR) or peak SNR (PSNR). Although such measures often lead to optimal schemes (with respect to MSE or PSNR), they are known to remove image parts that has noise-like appearances. Furthermore, they treat image singularities such as sharp edges or high textures (that are more important visually and diagnostically) and other image parts (that are less important) uniformly. In contrast, multifractal measures proposed in this paper characterize image singularities. This means the measures pay attention more on important image features, such as sharp edges and high textures. Furthermore, it can distinguish noise from noise-like signals through their differences in their types of singularities. As a result, the measure can be used to assess image quality of sensitive images resulting from processing schemes. The paper shows various ways of defining the measure that reveals multifractality of different aspects of images. It reports the use of the multifractal measure to compare a joint photographic expert group (JPEG) scheme and a variant of differential pulse code modulation (DPCM) coding showing that the DPCM scheme is superior in multifractal preservation for comparable compression ratios. As a result, the DPCM coding has been selected for a database of aerial ortho 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.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

Image compression techniques are used to reduce the storage and transmission costs. Joint Photographic Experts Group (JPEG) is one of the most popular compression standards in the field of still image compression. In JPEG technique, an input image is decomposed using the DCT, quantized using quantization matrix and further compressed by using entropy encoding. Therefore, the objective of this paper is to carry out the performance analysis of JPEG on the basis of quantization tables. In this paper, we have employed the nelson algorithm for the generation of quantization table; and an attempt has been made for the identification of the best quantization table, because for digital image processing (DIP), it is necessary to discover a new quantization tables to achieve better image quality than the obtained by the JPEG standard. Research has shown that quantization tables used during JPEG compression can also be used to separate images that have been processed by software from those that have not been processed; and it is also used to remove JPEG artefacts or for JPEG recompression.

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

Due to the constrained bandwidth and storage capacity, medical images must be compressed before transmission and storage. However, the compression will reduce the image fidelity, especially when the image is compressed of lower bit rate, which cannot be tolerated in medical field. In this paper, the lossy compression performance of the new JPEG2000 and the more conventional Joint Photographic Experts Group (JPEG ) is studied and compared. The parameters used for comparison include the compression efficiency, peak signal-to-noise ratio (PSNR), picture quality scale (PQS), and mean opinion score (MOS). Four types of medical modalities are used: magnetic resonance imaging (MRI), ultrasound, X-ray and computed tomography (CT). Overall, the study shows that JPEG2000 compression is more acceptable and superior compared to JPEG. The compression threshold point is also roughly determined for each of the medical image type.

LSI Logic has developed a two-chip processor for the core operations of the proposed JPEG (Joint Photographic Experts Group) image coding standard which is able to encode and decode all legal bitstreams and images regardless of compression rate. The user may thus be ensured that a legal bitstream is being generated regardless of image contents or compression ratio. The L64735 and L64745 have been designed to bridge the gap between building-block solutions and single-chip solutions for JPEG image coders. The two-chip approach offers some performance advantages over more highly integrated chips, as well as an assurance that all images regardless of their compression rate or contents can be safely handled at video rates. >

DWT-3DRec: DeepJSCC-based wireless transmission for efficient 3D scene reconstruction using CityNeRF

The compression and decompression of continuous-tone images is important in document management and transmission systems. This paper considers an alternative image representation scheme, based on Gaussian derivatives, to the standard discrete cosine transformation (DCT), within a Joint Photographic Experts Group (JPEG) framework. Depending on the computer arithmetic hardware used, the approach developed might yield a compression/decompression technique twice as fast as the DCT and of (essentially) equal quality.

In recent years, internet revolution resulted in an explosive growth in multimedia applications. The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. Besides this, it is easier to modify and misuse the valuable information through hacking at the same time. Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form, image or text that is impressed onto paper, which provides evidence of its authenticity. In this paper an invisible watermarking technique (least significant bit) and a visible watermarking technique is implemented. This paper presents the general overview of image watermarking and different security issues. Various attacks are also performed on watermarked images and their impact on quality of images is also studied. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB. Keywords - Watermarking, Least Significant Bit (LSB), JPEG (Joint Photographic Experts Group), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

The joint photographic experts group (JPEG) is the most popular format among various digital image formats used in our daily life. Therefore, watermarking JPEG images is important and useful for many applications such as archive management and image authentication. Along with the embedding capacity and imperceptibility that have to be considered for uncompressed images, the storage size of the watermarked JPEG image should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we propose a new JPEG image watermarking in the data compression domain, in which only coefficients with values 1 and -1 are expanded to carry message bits, coefficients with values 2 and -2 are shifted, and all the others coefficients remain unchanged. Moreover, a block selection strategy based on the number of 1 and -1 coefficients in each $8 \times 8$ block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method, we can easily realize high embedding capacity and good visual quality. Furthermore, the storage size increase for the watermarked JPEG image is well lessened.

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with higher performance, clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec and the DCT based AGP image codec (DCT- AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7 dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform. The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT- AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low- complexity and high performance codec may provide a new direction for the implementation of image compression.

Wavelet is one method that can be used as a step to recognition an individual . Wavelet is used to perform an image feature extraction . In the process of image texture analysis , showed the value of the coefficients of a wavelet . The magnitude of the coefficients obtained using wavelet feature extraction is influenced by several factors . Besides affected by the type of wavelet used , is also influenced by the magnitude of the wavelet decomposition level of itself. In the wavelet is known as the wavelet energy retention , which means the number of retained energy after undergoing a process of decomposition and cutting coefficients . During the decomposition process , the calculation for texture analysis is often a constraint . In order for the current calculation lighter texture analysis , necessary to the process of cutting coefficients based on the retention factor of the wavelet coefficients . Based on these issues , created a program to analyze the influence of variations in the level of wavelet decomposition and coefficient of variation coefficient of cutting a cut on the image . The object of this final project is 30 iris image that has been converted into polar form presented . Having experienced the process of cutting coefficients , to prevent the image of the reverse transformation has a big difference to the original image , in this study was calculated Mean Square Error ( MSE), Peak Signal to Noise Ratio ( PSNR), and Euclidean distance to determine the level of similarity of image input and output images . The Highest values of Retention ​, MSE and the Euclidean distance is obtained at the level of decomposition of 1 and the lowest at the level of decomposition 6 . While the truncated coefficient and PSNR values ​​obtained at the highest level of decomposition of 6 and the lowest level of decomposition of 6 . The variation coefficient of pieces , value retention and highest PSNR obtained at koefsien piece 5 and the lowest coefficient of 50 pieces . While the coefficient value is truncated , MSE and the highest Euclidean distance is obtained at koefsien pieces 50 and the lowest coefficient of 5 pieces . Keywords: iris, texture analysis, Haar wavelet transform, MSE, PSNR, E uclidean distance