Approach For Image Compression Research Articles

Learning Content-Weighted Deep Image Compression.

Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance, and requires to cope with the spatial variation of image content and contextual dependence among learned codes. Traditional entropy models can spatially adapt the local bit rate based on the image content, but usually are limited in exploiting context in code space. On the other hand, most deep context models are computationally very expensive and cannot efficiently perform decoding over the symbols in parallel. In this paper, we present a content-weighted encoder-decoder model, where the channel-wise multi-valued quantization is deployed for the discretization of the encoder features, and an importance map subnet is introduced to generate the importance masks for spatially varying code pruning. Consequently, the summation of importance masks can serve as an upper bound of the length of bitstream. Furthermore, the quantized representations of the learned code and importance map are still spatially dependent, which can be losslessly compressed using arithmetic coding. To compress the codes effectively and efficiently, we propose an upper-triangular masked convolutional network (triuMCN) for large context modeling. Experiments show that the proposed method can produce visually much better results, and performs favorably against deep and traditional lossy image compression approaches.

A Review on Fractal Image Compression Using Optimization Techniques

Image compression is an important process that has many possible application areas. The major blemish of fractal image compression is the time consuming compared to other image compression approaches. Image compression is the most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression techniques are responsible for decreasing the size of the image and keeping the quality of the recovered image. Presently many image compression algorithms are utilized to deal with the growing number of data concerned; still, area of research is to find an alternative solution to this problem. Therefore, to overcome this obstacle uses optimization techniques to solve this problem and reduces search space to find self-similarity in the given image. This review provides a study of many these techniques like Genetic Algorithm (GA), Crowding Optimization Method (COM), Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HAS) and how using them in fractal image compression.

Versatile Approaches for Medical Image Compression: A Review

In this world of evolution, the utilization of the medical technology and huge data generation by the different medical modalities, the storage, transmission and handling of the digital data requires data compression. In past few years, several compression mechanisms, techniques, and algorithms are proposed by many researchers. This paper focuses over the current status of these compression strategies with new hybrid approaches, performance parameters, legal issues and their applications in enhancing the medical technology.

Learning based Coding for Medical Image Compression

The area of Image processing has emerged with different coding approaches, and applications which are ranging from fundamental image compression model to high quality applications. The advancement of image processing, has given the advantage of automation in various image coding applications, among which medical image processing is one of the prime area. Medical diagnosis has always remained a time taking and sensitive approach for accurate medical treatment. Towards improving these issues, automation systems have been developed. In the process of automation, the images are processed and passed to a remote processing unit for processing and decision making. It is observed that, images are coded for compression to minimize the processing and computational overhead. However, the issue of compressing data over accuracy always remains a challenge. Thus, for an optimization in image compression, there is a need for compression through the reduction of non-relevant coefficients in medical images. The proposed image compression model helped in developing a coding technique to attain accurate compression by retaining image precision with lower computational overhead in clinical image coding. Towards making the image compression more efficient, this research work introduces an approach of image compression based on learning coding. This research achieves superior results in terms of Compression rate, Encoding time, Decoding time, Total processing time and Peak signal-to-noise ratio (PSNR).

Deep-Learning based Lossless Image Coding

This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.

Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps.

Image compression and image encryption are two essential tasks in image processing. The former aims to reduce the cost for storage or transmission of images while the latter aims to change the positions or values of pixels to protect image content. Nowadays, an increasing number of researchers are focusing on the combination of these two tasks. In this paper, we propose a novel joint image compression and encryption approach that integrates a quantum chaotic system, sparse Bayesian learning (SBL) and a bit-level 3D Arnold cat map, so-called QSBLA, for such a purpose. Specifically, the QSBLA consists of 6 stages. First, a quantum chaotic system is employed to generate chaotic sequences for subsequent compression and encryption. Second, as one method of compressive sensing, SBL is used to compress images. Third, an operation of diffusion is performed on the compressed image. Fourth, the compressed and diffused image is transformed into several bit-level cubes. Fifth, 3D Arnold cat maps are used to permute each bit-level cube. Finally, all the bit-level cubes are integrated and transformed into a 2D pixel-level image, resulting in the compressed and encrypted image. Extensive experiments on 8 publicly-accessed images demonstrate that the proposed QSBLA is superior or comparable to some state-of-the-art approaches in terms of several measurement indices, indicating that the QSBLA is promising for joint image compression and encryption.

Robust image processing algorithm for computational resource limited smart apple sunburn sensing system

Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses. Typically, when the fruit surface temperature (FST) rises above critical limits for a prolonged duration, the fruit may suffer several physiological disorders including sunburn. To manage apple sunburn, monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring. Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources. Therefore, key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer. Algorithm logic flow includes color space transformation, k-means++ classification and morphological operators prior to fruit segmentation and FST estimation. The developed algorithm demonstrated the segmentation accuracy of 57.78% (missing error = 12.09% and segmentation error = 0.13%). This aided successful apple FST estimation that was 10–18 °C warmer than ambient air temperature. Moreover, algorithm reduced the imagery data processing time cost of the smart sensing system from 87 s to 44 s using image compression approach.

An efficient lossy cartoon image compression method

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

A New Approach of Image Compression Techniques in Image Processing

In this research work to present the approach for image compression. The different techniques increasing attraction and dependence on the digital photograph have specified increase to that applying visual communication. That approach has to require a huge amount of information in the form of data. For the information, the transformation required less image size. The number of researches has been proposed approach for image compression but that approach destroys the original image quality. To propose an approach for image compression with the help of lossless and lossy.

The development of a clinically tested visually lossless Image compression system for capsule endoscopy

Background and objective:Capsule endoscopy investigations currently capture a large number of images that must be processed and transferred inside the capsule. This demands a compression system that is capable of reducing the amount of data processed, encoded and transmitted. In comparison to a lossless system, a near-lossless image compression approach would have lower complexity and reduce the amount of data processed inside the capsule. However, the visual quality of the images compressed using a near-lossless approach must be deemed suitable for diagnostic use by clinicians. Methods:This paper outlines the development and clinical validation of a near-lossless image compression approach based on a Simplified Structure Conversion method, an Optimized Difference Approach, and a Golomb–Rice encoder. Eighteen clinicians participated to assess the suitability of processed videos and images for diagnostic purposes. Results:Our near-lossless approach returned a Peak Signal to Noise Ratio (PSNR) of 43.89182 dB and Structural Similarity measure of 0.996746 and an overall compression ratio (CR) of 5:1. Clinicians assessed the videos compressed by the developed system and rated 82% of the videos as acceptable for diagnostic purposes. Clinicians also compared the images before and after processing with the developed system and rated 86% of the images as “no difference”. Conclusion:The high values of the MSSIM, PSNR, CR and the high acceptance of clinicians provided a reliable indication of the high performance of the developed compression system. This matches the image quality requirements of capsule endoscopy for real-world medical diagnosis.

Secure image compression approach based on fusion of 3D chaotic maps and arithmetic coding

Advances in digital image processing and communications have created a great demand for real-time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression-encryption systems are still a research problem as the intrinsic features of images. A new approach is suggested in this study for partial image encryption-compression that adopts chaotic three-dimensional (3D) cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilised as a lossy compression technique instead of using complex quantisation techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However, the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognisable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique.

An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach

In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.

Image compression in resource-constrained eye tracking devices

ABSTRACTResource-constrained embedded devices, operating with images, are becoming increasingly common. Examples include remote low-power smart sensors, wireless sensor networks, autonomous cameras, eye tracking devices, etc. The principal requirements of such devices are the operation in real time, low power, low heat as well as low MIPS. These requirements can be fulfilled with the use of approximated version of original image processing algorithms. The EyeDee™ embedded eye tracking solution (developed by SuriCog) is the world's first innovative solution using the eye as a real-time mobile digital cursor, while maintaining full mobility. Being an example of resource-constrained embedded device, the system consists of a wearable device (Weetsy™ frame) capturing images on the human's eye and an embedded pre-processing device (Weetsy™ pre-processing board) sending these eye images over a transmission medium (wire/wireless transmission) to a remote processing unit for the further gaze reconstruction. This paper is aimed at introducing image compression approaches in the resource-constrained devices in general and some of their implementation in the Weetsy™ pre-processing board in particular.

Light Field Image Compression Using Generative Adversarial Network-Based View Synthesis

Light field (LF) has become an attractive representation of immersive multimedia content for simultaneously capturing both the spatial and angular information of the light rays. In this paper, we present a LF image compression framework driven by a generative adversarial network (GAN)-based sub-aperture image (SAI) generation and a cascaded hierarchical coding structure. Specifically, we sparsely sample the SAIs in LF and propose the GAN of LF (LF-GAN) to generate the unsampled SAIs by analogy with adversarial learning conditioned on its surrounding contexts. In particular, the LF-GAN learns to interpret both the angular and spatial context of the LF structure and, meanwhile, generates intermediate hypothesis for the unsampled SAIs in a certain position. Subsequently, the sampled SAIs and the residues of the generated-unsampled SAIs are re-organized as pseudo-sequences and compressed by standard video codecs. Finally, the hierarchical coding structure is adopted for the sampled SAI to effectively remove the inter-view redundancies. During the training process of LF-GAN, the pixel-wise Euclidean loss and the adversarial loss are chosen as the optimization objective, such that sharp textures with less blurring in details can be produced. Extensive experimental results show that the proposed LF-GAN-based LF image compression framework outperforms the state-of-the-art learning-based LF image compression approach with on average 4.9% BD-rate reductions over multiple LF datasets.

High Speed 3d DWT VlSI Architecture for Image Processing Using Lifting Based Wavelet Transform

Although the DCT-based image compression method using in the JPEG standard has been very successful in the several years, it still has some properties to improvement. A fundamental shift in the image compression approach came after the discrete wavelet transform (DWT) became popular, and it is adopted in the new JPEG 2000 standard So the digital information must be stored and retrieved in an efficient and effective manner, in order for it to be put to practical use. The Discrete Wavelet Transform (DWT) was based on time-scale representation. It provides efficient multi-resolution. DWT has been implemented by convolution method. For Such an implementation it requires a large number of computations and a large storage features that are not suitable for either high-speed or low-power applications. Hence the architecture for a high speed lifting based 3D (DWT) VLSI architecture is proposed. The lifting based DWT architecture has the advantage of lower computational complexities and also requires less memory. This lifting scheme has several advantages, including in-place computation of the DWT, integer-to-integer wavelet transform (IWT), symmetric forward and inverse transform. It uses a combination of 1D-DWT along with a set of memory buffers between the stages. The whole architecture was arranged in efficient way to speed up and achieve higher hardware utilization. It is desirable for high-speed VLSI applications.

Improvement of image compression approach using dynamic quantisation based on HVS

Improvement of image compression approach using dynamic quantisation based on HVS

Improvement of image compression approach using dynamic quantisation based on HVS

Digital-image compression can reduce the overall volume of the image by keeping the original image with the minimum degradation in the level of the reconstructed image quality; in other words, here, we speak about compression with loss. This work comes up with an improvement in an image compression method using the discrete wavelet transform (DWT) and neural networks. To improve this technique, we have added a new phase based on the Human Visual System (HVS) and the Weber-Fechner law to dynamically quantify the image signal. Such a new phase can improve the quality of compression by dynamically quantifying each pixel value of the original image compared to the values of the neighbour pixels according to a luminance detection threshold. This threshold is known as Weber constant.

An Efficient Image Compression Approach based on DWT with Quality Parameter (PSNR)

An Efficient Image Compression Approach based on DWT with Quality Parameter (PSNR)

A Novel Hybrid Approach for Medical Image Compression

A Novel Hybrid Approach for Medical Image Compression

Turtle edge encoding and flood fill based image compression scheme

Over the last two decades, great improvements have been made in image and video compression techniques driven by a growing demand for storage and transmission of visual information. This paper focuses on image compression, the main objective of an image compression technique is to remove as much redundant information as possible without destroying the image integrity. This paper proposes an edge based image compression scheme for cartoon images. Initially the edges of the image are identified using zero-crossings edge detector, and the edges are decoded by using a novel encoder based on turtle graphics. From the edge map the closed regions are labelled to estimate the color quantization levels. However the isolated edges falls inside the closed regions are stored separately and the region is encoded with its color/gray value at a random seed pixel. While decoding the image, a flood-fill algorithm is used to fill each region by its corresponding color, starting from the seed point. The boundary of each region is marked with the edge contour (only for the closed regions), and the isolated edges are marked over the decoded image from the original edge map. The proposed Turtle Edge encoder and flood-fill based image compression approach is analyzed with a collection of cartoon images. The performance of the proposed compression method is compared with the state-of-art compression methods like JPEG and JPEG2000 and the recent algorithms, the experimental results indicate that the proposed turtle edge and flood-fill based approach is able to achieve better compression ratio within less computation time.