Lossless Image Compression Research Articles

Bit‐Error Aware Lossless Image Compression with 2D‐Layer‐Block Coding

With IoT development, it becomes more popular that image data is transmitted via wireless communication systems. If bit errors occur during transmission, the recovered image will become useless. To solve this problem, a bit‐error aware lossless image compression based on bi‐level coding is proposed for gray image compression. But bi‐level coding has not considered the inherent statistical correlation in 2D context region. To resolve this shortage, a novel variable‐size 2D‐block extraction and encoding method with built‐in bi‐level coding for color image is developed to decrease the entropy of information and improve the compression ratio. A lossless color transformation from RGB to the YCrCb color space is used for the decorrelation of color components. Particularly, the layer‐extraction method is proposed to keep the Laplacian distribution of the data in 2D blocks which is suitable for bi‐level coding. In addition, optimization of 2D‐block start bits is used to improve the performance. To evaluate the performance of our proposed method, many experiments including the comparison with state‐of‐the‐art methods, the effects with different color space, etc. are conducted. The comparison experiments under a bit‐error environment show that the average compression rate of our method is better than bi‐level, Jpeg2000, WebP, FLIF, and L3C (deep learning method) with hamming code. Also, our method achieves the same image quality with the bi‐level method. Other experiments illustrate the positive effect of built‐in bi‐level encoding and encoding with zero‐mean values, which can maintain high image quality. At last, the results of the decrease of entropy and the procedure of our method are given and discussed.

An optimised clustering algorithm with dual tree DS for lossless image compression

The emerging utilisation of web and other electronic applications have expedited much consideration on image compression systems to spare storage room and diminish transmission time by compressing the size of an image by discarding the repetitive data sequences. Most of the techniques are based on lossy compression techniques where the compression ratio would be low. The proposed system based on lossless compression technique achieves best compression ratio, good image quality and less PSNR value by extracting the best features of an image, which is to be compressed and encoded by incorporating firefly algorithm with k means algorithm there by avoids local optima problem. To make the eminent compression of best derived features, quad tree decomposition and Huffman encoding technique are combined, which provide high compression ratio by fetching correct probabilities of occurrence of pixel intensity. This proposed technique is actualised in MATLAB and in this manner the trial results demonstrated the effectiveness of the proposed image compression technique regarding high compression ratio, low noise ratio, and reduced compression and decompression time when compared with existing techniques.

An optimised clustering algorithm with dual tree DS for lossless image compression

An optimised clustering algorithm with dual tree DS for lossless image compression

Design and Implementation of Lossless Compression System for CCSDS Hyperspectral Images

Based on the xc7k325tffg900 FPGA chip of Xilinx Company and combining the actual hyperspectral image compression task requirements, the calculating speed and compression efficiency of the algorithm module are optimized. In the design of the prediction module in this article, the two-clock solution is used to solve the problem that the algorithm speed is restricted due to the feedback of the weight update. And the RTL-level design is implemented on the FPGA. CCSDS 123.0-B-1 hyperspectral image lossless compression algorithm is achieved. The system uses block compression to deal with the data source to avoid the entire system compression data errors caused by a certain pixel compression error. So the system’s Error Resilience ability is improved. Finally the compressed code stream is decoded. The decompressed image is consistent with the raw one, which verifies the correctness of this design.

Intelligent compression for synchrotron radiation source image

Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we propose a lossless compression method for SRS images based on deep learning. First, we use the difference algorithm to reduce the linear correlation within the image sequence. Then we propose a reversible truncated mapping method to reduce the range of the pixel value distribution. Thirdly, we train a deep learning model to learn the nonlinear relationship within the image sequence. Finally, we use the probability distribution predicted by the deep leaning model combined with arithmetic coding to fulfil lossless compression. Test result based on SRS images shows that our method can further decrease 20% of the data size compared to PNG, JPEG2000 and FLIF.

A Fast Method of Visually Lossless Compression of Dental Images

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

Lossless Image Compression Using Wavelet and Vector Quantization.(Dept.E)

In this paper, we propose a new low bit rate scheme that combines the Discrete Wavelet transform (IWT) and Vector Quantization (VQ) for image compression with high Compression Ratio (CR) in order to achieve acceptable quality of output images, Image compression using the combination of DWT willy VQ has been considered in many recent works; here in our paper the DWT was performed on several gray scale standard images, each of (256X256) byte. We used 4 images as the training images of the train VO using LBG algorithm and got the final optimal codebook. Then we used another two images to compare our output image with JPEG 2000 standard winch based on Wavelet and Scalar quantization. Our proposed system used a VO codebook with Variable cod hook size and dimensions were used to compress wavelet Transform) coefficients resulting using Danny wavelet types. Our pro system is slowed so that the best Wavelet types that you can use for compression is COIF 2. Our proposed system gave better PSNR than G2000 standard at high (CR), above than (5L), and this achieved a VQ codebook size (1024 -512) with dimension equal to 256. In the paper a typical system for the application of image compression system and new scientific results are present.

Near-lossless image compression using an improved edge adaptive hierarchical interpolation

<span>In medical and scientific imaging, lossless image compression is recommended because the loss of minor details subject to medical diagnosis can lead to wrong diagniosis. On the other hand, lossy compression of medical images is required in the long run because a huge quantity of medical data needs remote storage. This, in turn, takes long time to search and transfer an image. Instead of thinking lossless or lossy image compression methods, near-loss image compression mehod can be used to compromise the two conflicting requirements. In the previous work, an edge adaptive hierarchical interpolation (EAHINT) was proposed for resolution scalable lossless compression of images. In this paper, it was enhanced for scalable near-less image compression. The interpolator of this arlgorithm swiches among one-directional, multi-directional and non-directional linear interpolators adaptively based on the strength of the edge in a 3x3 local casual context of the current pixel being predicted. The strength of the edge in local window was estimated using the variance of the the pixels in the local window. Although the actual predictors are still linear functions, the switching mechanism tried to deal with non-linear structures like edges. Simulation results demonstrate that the improved interpolation algorithm has better compression ratio over the the exsisting the original EAHINT algorithm and JPEG-Ls image compression standard. </span>

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.

An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.

Hypercolorimetric multispectral Imaging and Pulse Compression thermography as innovative combined techniques for painting investigation: the case of a detached wall painting by Pastura

This contribution focuses the attention on an innovative approach in diagnostics of paintings, based on the combine use of two imaging techniques named Hypecolorimetric Multispectral Imaging (HMI) and Pulse Compression Thermography (PuCT) applied to a 15th century wall painting, attributed to the Italian artist Antonio del Massaro, also known as Pastura. HMI technique is based on the simultaneous exploitation of the electromagnetic spectrum from the ultraviolet to the near infrared region. The acquisition, made under a standard metric, allows for characterizing the investigated surfaces in a more detailed way than the standard colorimetry. The system transforms any spectra in the range 300-1000nm into sevenfold hypecolorimetric coordinates. HMI guarantees very high radiometric (better than 95%) and colorimetric precision (better than ΔE = 2). PuCT is a thermography technique based on the use of coded modulated heating stimuli in combination with the pulse-compression technique. A PuCT scheme, based on coded LED excitation capable of optimizing the estimation of the impulse responses compared to the state-of-the-art PuCT literature has also been proposed. The combined use of HMI and PuCT recently revealed its potentiality in the investigation of important panel paintings by highlighting hidden details, mapping the conservation status, characterizing painting materials, etc. in a completely non-invasive way. Their combined capabilities are here tested on a wall painting representing the Madonna with the Child and the Saints Jerome and Francis, which was investigated during the restoration in the Laboratory in order to supply information about the materials and techniques.

Medical Images Compression and Decompression Using Neural Networks

In Emergency situations medical images of different types have to be transferred from one place to another place and the images with larger size are difficult totransfer because they are time consuming and occupies more storage. So we need image compression and decompression so that the medical images can be easily transferred and by using lossless image compression we can get the original data without any loses at receiver side .Image compression can be made possible by using normal forward transform techniques. But we cannot load multiple images at a time. So here we are using Support Vector Machine Neural network technique for image compression through which we can load multiple images which reduces the transmission time.

Lossless Compression for Hyperspectral Images based on Adaptive Band Selection and Adaptive Predictor Selection

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform.

A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation. For a large and diverse test set consisting of 499 photographic and 247 non-photographic (screen content) images, we found that RDLS with step skipping allowed effectively combining DWT with prediction. Using prediction, we nearly doubled the JPEG 2000 compression ratio improvements that could be obtained using RDLS with step skipping. Because for some images it might be better to apply prediction instead of DWT, we proposed compression schemes with various tradeoffs, which are practical contributions of this study. Compared with unmodified JPEG 2000, one scheme improved the compression ratios of photographic and non-photographic images, on average, by 1.2% and 30.9%, respectively, at the cost of increasing the compression time by 2% and introducing only minimal modifications to JPEG 2000. Greater ratio improvements, exceeding 2% and 32%, respectively, are attainable at a greater cost.

Lossless Hybrid Coding technique based on Quasi Fractal &amp; Oscillation Concept Method for Medical Image Compression

The Image compression is the most important entity in various fields. Image compression plays vital role in many applications. Out of which biomedical is one of the challenging applications. In medical research, everyday there is fast development and advancement. Medical researchers are thinking about digital storage of data hence medical image compression has a crucial role in hospitals. Here Morphological filter &amp; adaptive threshold are used for refinement and used Quasi Fractal &amp; Oscillation concept for developing new hybrid algorithm. Oscillation concept is lossy image compression technique hence applied on Non-ROI. Quasi fractal is lossless image compression technique applied on ROI. The experimental results shows that better CR with acceptable PSNR has been achieved using hybrid technique based on Morphological band pass filter and Adaptive thresholding for ROI. Here, innovative hybrid technique gives the CR 24.61 which improves a lot than hybrid method using BTC-SPIHT is 5.65. Especially PSNR is also retained and bit improved i.e. 33.51. This hybrid technique gives better quality of an image.

Spatially and Spectrally Concatenated Neural Networks for Efficient Lossless Compression of Hyperspectral Imagery.

To achieve efficient lossless compression of hyperspectral images, we design a concatenated neural network, which is capable of extracting both spatial and spectral correlations for accurate pixel value prediction. Unlike conventional neural network based methods in the literature, the proposed neural network functions as an adaptive filter, thereby eliminating the need for pre-training using decompressed data. To meet the demand for low-complexity onboard processing, we use a shallow network with only two hidden layers for efficient feature extraction and predictive filtering. Extensive simulations on commonly used hyperspectral datasets and the standard CCSDS test datasets show that the proposed approach attains significant improvements over several other state-of-the-art methods, including standard compressors such as ESA, CCSDS-122, and CCSDS-123.

Two-dimensional optical scanning for large-field-of-view optical image generation applications

Optical image generation is a very important step in many optical imaging system applications. However, current optical image generation systems (OIGS) produce a fixed simple grid image with a small field of view (FOV) because of the physical limitation of the OIGS itself. Here, we use proposed an optical image generation method using optical scanners to achieve a large FOV and images defined by the user. The combination of two scanners implements the two-dimensional optical scanning for obtaining the improvement of field-of-views (FOV) in two-dimensional (2D) directions. Here, three important findings, including scanning optical configurations, an analog-to-digital-based scanning pattern, and a scanning imaging model, of 2D optical scanning in the image generation with a large field of view are reported. The proposed method can improve the FOV with 20° in the 2D directions. These three results are very valuable references for the application of image generation. The proposed method has potential applications in multiple fields, such as camera calibration, computation imaging, and image compression.

Gbit/s Throughput Under 6.3-W Lossless Hyperspectral Image Compression on Parallel Embedded Devices

The consultative committee for space data system (CCSDS)-123 is a standard for lossless compression of multispectral and hyperspectral images with applications in on-board power-constrained systems, such as satellites and military drones. This letter explores the low-power heterogeneous architecture of the Nvidia Jetson TX2 by proposing a parallel solution to the CCSDS-123 compressor on embedded systems, reducing development effort compared with the production of dedicated circuits, while maintaining low energy consumption. This solution parallelizes the predictor on a low-power graphics processing unit (GPU) while the encoders exploit the heterogeneous multiple cores of the CPUs and GPU concurrently. We report more than 16.6 Gb/s for the predictor and 1.4-Gb/s for the whole system, requiring less than 6.3 W and providing an efficiency of 245.6 Mb/s/W.

Lossless Compression of Medical Images based on the Differential Probability of Images

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.

Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression

In this paper, a new method for automatic filter coefficient calculation in lifting scheme wavelet transform for image lossless compression is proposed. Actually, there is no specific rule for setting filter coefficients (a, b). Therefore, this work proposes an automatic method to calculate the filter coefficients depending on the spectral analysis of each image. Also, filter coefficients are determined for five decomposition levels and for each quadrant through applying the discrete wavelet transform in the lossless image compression problem. Spectral patterns are computed and fixed into small length vectors for building different wavelet decomposition levels; these vectors are automatically computed using a 1-NN classifier. Experimental results over standard images show that calculating the wavelet filter coefficients using the proposed method generates higher compression rates (in entropy and bitstream values) against standard wavelet and linear prediction filters.