Approach For Image Compression Research Articles

A near-lossless approach for medical image compression using visual quantisation and block-based DPCM

For the efficient transmission of medical image datasets over the internet, compression techniques which offer high compression ratio and less computational cost are required. Since the existing lossless techniques have low compression ratios, the alternative approach is to employ near-lossless methods. The existing near-lossless methods like JPEG-LS are context based and computationally intensive. In this paper, a new approach for near-lossless compression of the medical images is proposed. Pre-processing techniques are applied to the input image to generate a visually quantised image. The visually quantised image is encoded using a low complexity block-based lossless differential pulse code modulation coder, followed by the Huffman entropy encoder. The compression performance is compared with the state-of-the-art technique context-based adaptive lossless image coding, by using the objective analysis parameter peak signal to noise ratio and the image fidelity measurement parameter visual signal to noise ratio. Results show the superiority of the proposed technique in terms of the bit rate and visual quality.

Kolmogorov superposition theorem for image compression

The authors present a novel approach for image compression based on an unconventional representation of images. The proposed approach is different from most of the existing techniques in the literature because the compression is not directly performed on the image pixels, but is rather applied to an equivalent monovariate representation of the wavelet-transformed image. More precisely, the authors have considered an adaptation of Kolmogorov superposition theorem proposed by Igelnik and known as the Kolmogorov spline network (KSN), in which the image is approximated by sums and compositions of specific monovariate functions. Using this representation, the authors trade the local connectivity and the traditional line-per-line scanning, in exchange of a more adaptable and univariate representation of images, which allows to tackle the compression tasks in a fundamentally different representation. The contributions lie in the several strategies presented to adapt the KSN algorithm, including the monovariate construction, various simplification strategies, the proposal of a more suitable representation of the original image using wavelets and the integration of this scheme as an additional layer in the JPEG 2000 compression engine, illustrated for numerous images at different bit rates.

Five Modulus Method for Image Compression

Data is compressed by reducing its redundancy, but this also makes the data less reliable, more prone to errors. In this paper a novel approach of image compression based on a new method that has been created for image compression which is called Five Modulus Method (FMM). The new method consists of converting each pixel value in an 8-by-8 block into a multiple of 5 for each of the R, G and B arrays. After that, the new values could be divided by 5 to get new values which are 6-bit length for each pixel and it is less in storage space than the original value which is 8-bits. Also, a new protocol for compression of the new values as a stream of bits has been presented that gives the opportunity to store and transfer the new compressed image easily.

Enhancement of Compression Ratio and Image Quality using ISPIHT with MFHWT

This paper presents a new approach for compression of medical images with MFHWT (Modified Fast Haar Wavelet Transform) and SPIHT (Set Partitioning In Hierarchical Trees). It provide high compression ratio with high picture quality. The Modified Fast Haar Wavelet Transform is used to decompose the image at different frequency levels. It has high multi-resolution characteristics. The CR (compression ratio) of proposed method is better than existing method (SPIHT). Medical images have a number of regions where intensity is changing slowly or even have a constant value. Such regions are compressed with higher compression ratio. In medical applications image distortion is not acceptable. So the quality of the images is also improved in proposed method in terms of PSNR (Peak Signal to Noise Ratio).

Effective Image Compression Techniques using Pre-filtering of Daubechies Transform

Image compression has become an active area of research in the field of Image processing particularly in the applications of medical and space sciences. Due to its importance, several image compression techniques were developed in the literature. But, majority of the conventional image compression approach available in the literature have poor visual quality and low PSNR value. In recent years, wavelet transform is observed to be very efficient in improving the performance of the image compression techniques. The main advantages of wavelet based methods lie in their energy compaction property and multi-resolution decomposition capability. The existing Haar transform based image compression approach has very low computational complexity but its compression performance is not significant. Improved SPIHT algorithm is used in this approach for the effective image compression of the image. This paper introduces a novel image compression approach using effective Daubechies Wavelet transform. For best performance in image compression, pre-filtering of wavelets transform is performed that combine a number of desirable properties, such as orthogonality and symmetry. The experimental results reveal that PSNR value and the visual quality of the proposed Improved SPIHT are very significant.

A Natural Image Compression Approach Based on Independent Component Analysis and Visual Saliency Detection

In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust than DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or lowly compressed while the other regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.

An Improved Neural Image Compression Approach With Cluster Based Pre-Processing

The convergence time for training back propagation neural network for image compression is slow as compared to other traditional image compression techniques. This article proposes a pre-processing technique i.e. Pre-processed Back propagation neural image compression (PBN) with an enhancement in performance measures like better convergence time with respect to decoded picture quality and compression ratios as compared to simple back-propagation based image compression and other image coding techniques for color images.

A Natural Image Compression Approach Based on ICA and Visual Saliency Detection

In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust to DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or low compressed while the other regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.

Image Compression using Haar and Modified Haar Wavelet Transform

Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5) to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle), and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform). The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform) algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R), achieving higher peak signal to noise ratio (PSNR), and the resulting images are of much smoother as compared to standard JPEG especially for high C.R.A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally, which approves the highest capability of Modified Haar between others

Combinatorial transforms: Applications in lossless image compression

Common image compression standards are usually based on frequency transform such as Discrete Cosine Transform. We present a different approach for lossless image compression, which is based on a combinatorial transform. The main transform is Burrows Wheeler Transform (BWT) which tends to reorder symbols according to their following context. It becomes one of promising compression approach based on context modeling. BWT was initially applied for text compression software such as BZIP2; nevertheless it has been recently applied to the image compression field. Compression schemes based on the Burrows Wheeler Transform have been usually lossless; therefore we implement this algorithm in medical imaging in order to reconstruct every bit. Many variants of the three stages which form the original compression scheme can be found in the literature. We propose an analysis of the latest methods and the impact of their association and present an alternative compression scheme with a significant improvement over the current standards such as JPEG and JPEG2000.

New Approaches for Image Compression Using Neural Network

An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.

An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression

This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach (Full 3D and Hybrid). All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed. Results of this comparison show the weaknesses and strengths of each approach.

Compression of multispectral and multi-view images with inverse pyramid decomposition

In this paper, a new approach for compression of multi-view and multispectral images, based on the Inverse Pyramid Decomposition (IPD), is presented. This approach is applicable to large number of spectral images or views of same object, processed as a common group. For this, their histograms are calculated and compared. The image, whose histogram is most similar with these of the remaining ones in the group, is used as a reference. The image decomposition starts with the reference image, which is processed with some kind of orthogonal transform, using limited number of transform coefficients only and after inverse transform is obtained the coarse approximation of the image. The IPD then branches out into several parts, corresponding to number of images in the group. The first approximation used in the group is that, calculated for the reference image. In result is obtained high compression and very good visual quality of the restored images.

Block-Based Image Compression With Parameter-Assistant Inpainting

This correspondence presents an image compression approach that integrates our proposed parameter-assistant inpainting (PAI) to exploit visual redundancy in color images. In this scheme, we study different distributions of image regions and represent them with a model class. Based on that, an input image at the encoder side is divided into featured and non-featured regions at block level. The featured blocks fitting the predefined model class are coded by a few parameters, whereas the non-featured blocks are coded traditionally. At the decoder side, the featured regions are restored through PAI relying on both delivered parameters and surrounding information. Experimental results show that our method outperforms JPEG in featured regions by an average bit-rate saving of 76% at similar perceptual quality levels.

블록화 현상의 측정을 통한 영상의 화질평가

ABSTRACT Block based transform coding is most popular approach for image and video compression. However it suffers from severe quality degradation especially from blocking artifacts. The subjective quality degradation caused by such blocking artifacts in general does not agree well with an objecive quality measurement such as PSNR. Hence new quality evaluation technique is necessary. We propose a new image quality assessment method by measuring blocking artifacts for block based transform coded images. In order to characterize blocking artifacts, proposed method utilizes the facts that, blocking artifacts, when occur, have different pixel values along the block boundaries and such differences usually continuously span along the whole boundaries. This method does not require the original uncompressed image. It operates on single block boundary and quantifies the amount of blocking artifacts on it. Experiments on various compressed images various bitrates show that proposed quantitative measure of blocking artifacts matches well with the subjective quality of them judged by human visual system.Keywords:Blocking artifacts, Blockiness measure, Quantitative metric, Image quality assessment

Ｃｏｌｏｒｉｚａｔｉｏｎを用いた画像圧縮符号化のための色指定情報抽出法

Novel image compression approaches that use a process called colorization, which is a method for adding color to a monochrome image, has recently been proposed. Almost all the proposed methods represent color information by using a set of color points. However, in an area with flat chrominance and variant luminance, we need redundant points to correctly decode color. We propose a color information extraction method that represents color information as a set of color lines. To effectively extract lines from an input image, color clustering and tentative color information have been introduced. The experimental results show that our proposed method allows us to drastically suppress the color information compared to existing colorization-based coding and conventional JPEG compression.

A Novel Fractal Wavelet Image Compression Approach

By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the different levels of importance accorded the frequency sublevel band wavelet coefficients. Higher frequency sublevel bands would lead to larger initial errors. As a result, the sizes of sublevel blocks and super blocks would be changed according to the initial errors. The matching sizes between sublevel blocks and super blocks would be changed according to the permitted errors and compression rates. Systematic analyses are performed and the experimental results demonstrate that the proposed method provides a satisfactory performance with a clearly increasing rate of compression and speed of encoding without reducing SNR and the quality of decoded images. Simulation results show that our method is superior to the traditional wavelet tree based methods of fractal image compression.

Using edge direction information for measuring blocking artifacts of images

Block-based transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring blocking artifacts in images and videos. Instead of using the traditional pixel discontinuity along the block boundary, we use the edge directional information of the images. The new algorithm does not need the exact location of the block boundary thus is invariant to the displacement, rotation and scaling of the images. Experiments on various still images and videos show that the new blockiness measure is very efficient in terms of computational complexity and memory usage, and can produce blocking artifacts measurement consistent with subjective rating.

Optical image compression based on segmentation of the Fourier plane: new approaches and critical analysis

We present a new optical image compression approach based on spectral selection and using a segmented filter. In order to identify the method offering the best quality of reconstruction of the optically compressed images, we investigated several spectral segmentation methods. The compression is carried out in the spectral domain and results in a compound spectrum referred to as the segmented spectrum. Indeed, relevant information should be selected from each spectrum to form a unique spectrum that is able to reconstruct the respective images. To separate the images in the output plane, after applying a second Fourier transform, the segmented spectrum should include a distinct carrier for each spectrum. Information selection consists of comparing pixel by pixel the spectra of the images being compressed. To identify the winner among the spectral pixels, we investigated four forms of information, namely the intensity of the pixel in question, its complex gradient, its phase gradient and its real part. In addition to these forms, we considered three options of selection, namely selection of the pixel with the most important amount of information, selection with respect to a fixed probability function and selection based on a matched probability function.

An improved fractal image compression approach by using iterated function system and genetic algorithm

This paper presents an improved method of generating a binary image affine IFS (iterated function system) by using genetic algorithm. We adopt a natural variable-length genotype encoding to represent an individual. The multiobject fitness function is also applied in this algorithm. In addition, a distributed version of the binary image compression algorithm is implemented. Both theoretical analysis and experimental results show a higher compression ratio with better quality images by using the proposed algorithm.