Image Compression using Approximate Matching and Run Length

Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human vision if the correct key image is used. In Visual Cryptography the reconstructed image after decryption process encounters a major problem of Pixel expansion. This is overcome in this proposed method by minimizing the memory size using lossless image compression techniques. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. Hybrid techniques are used in this proposed method as it can exploit multiple kinds of redundant information Keywords: Visual Cryptography; HVS; Image Compression; Vector Quantization; Run Length Encoding; Huffman Coding

Image compression applications attempts to deepen the number of bits imperative to digitally symbolize an image while maintaining its articulate visual excellence. Image compression is a procedure that is very vastly used for the integral and resourceful convey of data. Image compression application and techniques used for image to be send over to the internet. It not only reduces the dimension of realistic file to be transferred but at the equivalent time reduces the storage space requirements, cost of the data transferred, actual form of data, clearly displayed and the time required for the transfer. It makes the diffusion progression faster, provides superior bandwidth and security beside illegitimate use of data. Image compression has divide in two type's lossy image compression and lossless image compression. In lossy image compression there is no loss of data and lossless image compression is used to retain original multimedia object.

Compression purpose to reduce the redundancy data as small as possible and speed up the data transmission process. To solve the size problem in saving data and transmission process, we use Run Length Encoding and Fibonacci Code algorithm to do compression process. Run Length Encoding and Fibonacci Code algorithm is a type of lossless data compression used in this research, which performance will be measured by comparison parameters of the Compression Ratio (CR), Redundancy (RD), Space Saving (SS) and Compression Time. The compression process is only done on image files with Bitmap format (*.bmp) and encode using Run Length Encoding or Fibonacci Code, then perform the compression process. The final result of the compression is file with extension *.rle or *.fib which contains compressed information that can be decompressed back. The output of the decompression result is an original image file that is stored with *.bmp extension. Fibonacci algorithm will give a better compressed size on image color, while in a grayscale image Run Length Encoding will give a better compressed size. Based on the results of research at two different types of images, each algorithm has its own advantages. Fibonacci Code algorithm is better for color image compression while Run-Length algorithm Encoding is better for grayscale image compression.

Lossless image compression has traditionally employed techniques quite separate from those used for text compression or lossy image compression; most standards employ modeling followed by coding (e.g., the JBIG standard, the IBM Q-coder, CCITT Group 4). Constantinescu and Storer [1994] presented a lossy image compression scheme that can be viewed as a generalization of lossless dynamic dictionary compression ("LZ2" type methods) to two dimensions with approximate matching; Constantinescu and Storer [1995] have experimented with this approach for lossless image compression with great success. Here we generalize "LZ1" type methods to lossless image compression. We examine complexity issues and 2D implementations.

The Run Length Encoding (RLE) algorithm substitutes long runs of identical symbols with the value of that symbol followed by the binary representation of the frequency of occurrences of that value. This lossless technique is effective for encoding images where many consecutive pixels have similar intensity values. One of the major problems of RLE for encoding runs of bits is that the encoded runs have their lengths represented as a fixed number of bits in order to simplify decoding. The number of bits assigned is equal to the number required to encode the maximum length run, which results in the addition of padding bits on runs whose lengths do not require as many bits for representation as the maximum length run. Due to this, the encoded output sometimes exceeds the size of the original input, especially for input data where in the runs can have a wide range of sizes. In this paper, we propose VaFLE, a general-purpose lossless data compression algorithm, where the number of bits allocated for representing the length of a given run is a function of the length of the run itself. The total size of an encoded run is independent of the maximum run length of the input data. In order to exploit the inherent data parallelism of RLE, VaFLE was also implemented in a multithreaded OpenMP environment. Our algorithm guarantees better compression rates of upto 3X more than standard RLE. The parallelized algorithm attains a speedup as high as 5X in grayscale and 4X in color images compared to the RLE approach.

Lossless image compression has often employed techniques quite separate from those used for text compression or lossy image compression; most standards today employ modelling followed by coding (e.g. the JBIG standard, the IBM Q-coder, CCITT Group 4). Constantinescu and Storer present a lossy image compression scheme that can be viewed as a generalization of lossless dynamic dictionary compression ('LZ2'-type methods) to two dimensions with approximate matching; recently, Constantinescu and Storer have experimented with this approach for lossless bi-level image compression with great success. Here we generalize 'LZ1'-type methods (that identify matches in previously seen text) to lossless image compression. We examine complexity issues and finish by considering practical 2-D implementations for bi-level images.

Lossless image compression based on integer Discrete Tchebichef Transform

The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images.

Mammography is a low dose x-ray technique that's used to create an image of the breast. It is an efficient way for early detection of any cancerous changes and malignancy of lumps. Mammographic images are usually archived and in many cases are transferred on Internet. Therefore, compression of these images has attracted the attention of many researchers. In this paper an efficient method is proposed for lossless compression of mammographic images. Gradual compression of prediction errors in an iterative manner is the basic idea of the proposed method. The simulation results were compared with standard image compression routines such as JPEG-LS, JPEG2000, JBIG and PNG. The superiority of the proposed method was shown for compression of mammographic images.

Image compression is a very important useful technique for efficient transmission as well as storage of images. The demand for communication of multimedia data through the telecommunication network and accessing the multimedia data through internet by utilizing less bandwidth for communication is growing explosively. Basically the image data comprise of significant portion of multimedia data and they occupy maximum portion of communication bandwidth for multimedia communication. Therefore the development of efficient image compression technique is quite necessary. The 2D Haar wavelet transform along with Hard Thresholding and Run Length Encoding is one of the efficient proposed image compression technique. JPEG2000 is a standard image compression method capable of producing very high quality compressed images. Conventional Run Length Encoding(CRLE),Optimized Run Length Encoding(ORLE),Enhanced Run Length Encoding(ERLE) are different types of RLES applied on both proposed method of compression and JPEG2000. Conventional Run Length Encoding produces efficient result for proposed method whereas Enhanced Run Length Encoding produces efficient result in JPEG2000 compression. This is the novel approach that the authors have proposed for compression of image using compression ratio (CR) without losing the PSNR, quality of image using lesser bandwidth.

We present a general purpose lossless greyscale image compression method, TMW, that is based on the use of linear predictors and implicit segmentation. We then proceed to extend the presented methods to cover near lossless image compression. In order to achieve competitive compression, the compression process is split into an analysis step and a coding step. In the first step, a set of linear predictors and other parameters suitable for the image is calculated, which is included in the compressed file and subsequently used for the coding step. This adaption allows TMW to perform well over a very wide range of image types. Other significant features of TMW are the use of a one-parameter probability distribution, probability calculations based on unquantized prediction values, blending of multiple probability distributions instead of prediction values, and implicit image segmentation. For lossless image compression, the method has been compared to CALIC on a selection of test images, and typically outperforms it by between 2 and 10 percent. For near lossless image compression, the method has been compared to LOCO (Weinberger et al. 1996). Especially for larger allowed deviations from the original image the proposed method can significantly outperform LOCO. In both cases the improvement in compression is achieved at the cost of considerably higher computational complexity.

In this paper, a new improved low complexity compression methodology named as Near Lossless Image Compression (NLIC) has been suggested. This algorithm is a hybrid method which includes DCT, a lossy compression technique and JPEG encoding using entropy based Huffman coding, a lossless compression technique. The algorithm is tested with standard square images of sizes 512 × 512 and 1024 × 1024 respectively. The results of NLIC are also compared with the Huffman and Run length Encoding (RLE) for evaluating its compression efficiency.

We propose a novel joint lossy image and residual compression framework for learning ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> -constrained near-lossless image compression. Specifically, we obtain a lossy reconstruction of the raw image through lossy image compression and uniformly quantize the corresponding residual to satisfy a given tight ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> error bound. Suppose that the error bound is zero, i.e., lossless image compression, we formulate the joint optimization problem of compressing both the lossy image and the original residual in terms of variational auto-encoders and solve it with end-to-end training. To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks. We further correct the bias of the derived probability model caused by the context mismatch between training and inference. Finally, the quantized residual is encoded according to the bias-corrected probability model and is concatenated with the bitstream of the compressed lossy image. Experimental results demonstrate that our near-lossless codec achieves the state-of-the-art performance for lossless and near-lossless image compression, and achieves competitive PSNR while much smaller ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> error compared with lossy image codecs at high bit rates.

This Paper considers the design of lossless image and data compression methods dedicated to still images and text data. For Images, after a preprocessing step (RGB to gray transformation, resizing) and for text after a preprocessing step (ASCII conversion), dynamic Huffman and Run Length Encoding (RLE) is applied. The dynamic Huffman coding involves computing an approximation to the probabilities of occurrence “on the fly”, as the ensemble is being transmitted with the aim to obtain the best possible compression ratio CR and Time Elapsed to compress. The additional parameters of evaluation in case of images are PSNR and MSE. The efficiency of the proposed methods is verified by applying these techniques to variety of data and images. Motivation behind this work is to provide a detail analysis of lossless compression methods which can be best suited in cognitive radio environment.

For efficiency and security of image transmission and storage, the joint image compression and encryption method that performs compression and encryption in a single step is a promising solution due to better security. Moreover, on some important occasions, it is necessary to save images in high quality by lossless compression. Thus, a joint lossless image compression and encryption scheme based on a context-based adaptive lossless image codec (CALIC) and hyperchaotic system is proposed to achieve lossless image encryption and compression simultaneously. Making use of the characteristics of CALIC, four encryption locations are designed to realize joint image compression and encryption: encryption for the predicted values of pixels based on gradient-adjusted prediction (GAP), encryption for the final prediction error, encryption for two lines of pixel values needed by prediction mode and encryption for the entropy coding file. Moreover, a new four-dimensional hyperchaotic system and plaintext-related encryption based on table lookup are all used to enhance the security. The security tests show information entropy, correlation and key sensitivity of the proposed methods reach 7.997, 0.01 and 0.4998, respectively. This indicates that the proposed methods have good security. Meanwhile, compared to original CALIC without security, the proposed methods increase the security and reduce the compression ratio by only 6.3%. The test results indicate that the proposed methods have high security and good lossless compression performance.