Lossless Color Image Research Articles

Hierarchical lossless color image coding method using cellular neural networks based predictors

Hierarchical lossless color image coding method using cellular neural networks based predictors

Paper] Lossless Color Image Coding Based on Probability Model Optimization Utilizing Example Search and Adaptive Prediction

Paper] Lossless Color Image Coding Based on Probability Model Optimization Utilizing Example Search and Adaptive Prediction

Effective lossy and lossless color image compression with Multilayer Perceptron

This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel structure of neural network and the concept of image compression combined to yield a better reconstructed image with constant bit rate and less computation complexity. Original color image component has been divided into 8x8 blocks. The discrete cosine transform (DCT) applied on each block for lossy compression or discrete wavelet transform (DWT) applied for lossless image compression. The output coefficient values have been normalized by using mod function. These normalized vectors have been passed to Multilayer Perceptron (MLP). This proposed method implements the Back propagation neural network (BPNN) which is suitable for compression process with less convergence time. Performance of the proposed compression work is evaluated based on three ways. First one compared the performance of lossy and lossless compression with BPNN. Second one, evaluated based on different sized hidden layers and proved that increased neurons in hidden layer has been preserved the brightness of an image. Third, the evaluation based on three different types of activation function and the result shows that each function has its own merit. Proposed algorithm has been competed with existing JPEG color compression algorithm based on PSNR measurement. Resultant value denotes that the proposed method well performed to produce the better reconstructed image with PSNR value approximately increased by 21.62%.

A robust and lossless DNA encryption scheme for color images

In this paper, a new robust and lossless color image encryption algorithm is presented based on DNA sequence operation and one-way coupled-map lattices (OCML). The plain-image is firstly decomposed into three gray-level components and we randomly convert them into three DNA matrices by the DNA encoding rules. Then the XOR operation is performed on the DNA matrices for two times. Next, the shuffled DNA matrices are transformed into three gray images according to the DNA decoding rules. Finally, a diffusion process is further applied to change the image pixel’s values by a key stream, and the cipher-image is attained. The key stream generated by OCML is related to the plain-image. Experimental results and security analysis demonstrate that the proposed algorithm has a good encryption effect and can withstand various typical attacks. Furthermore, it is robust against some common image processing operations such as noise adding, cropping, JPEG compression etc.

Context-Based Predictor Blending for Lossless Color Image Compression

Images are typically nonstationary signals. If prediction is applied in a linear fashion, it must be combined with a technique that takes this characteristic into account. In general, images can either be regarded as piecewise 2-D autoregressive processes or they are handled in a blockwise manner. This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to deinterleave the sequence and to compute prediction weights for each subsource separately. The proposed approach adaptively determines the subsources based on the textures surrounding the pixels. The new linear prediction technique is combined with template-matching prediction and a blending method that considers the correlation between the predictors’ estimates is proposed. The prediction method is incorporated in a framework for lossless color image compression. In combination with an adaptive color transform and a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of natural color images.

A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system

This paper proposes a new lossless encryption algorithm for color images based on a six-dimensional (6D) hyperchaotic system and the two-dimensional (2D) discrete wavelet transform (DWT). Different from the current image encryption methods, our image encryption scheme is constructed using the 2D DWT and 6D hyperchaotic system in both the frequency domain and the spatial domain, where the key streams depend on not only the hyperchaotic system but the plain-image. In the presented algorithm, the plain-image is firstly divided into four image sub-bands by means of the 2D DWT. Secondly, the sub-bands are permutated by a key stream, and then the size of them is decreased by a constant factor. Thirdly, the 2D inverse DWT is employed to reconstruct an intermediate image by the four encrypted image sub-bands. Finally, to further enhance the security, the pixel values of the intermediate image are modified by using another key stream. Experimental results and security analysis demonstrate that the proposed algorithm has a high security, fast speed and can resist various attacks.

Lossless Color Image Compression using Double Level RCT in BBWCA

A new lossless image compression scheme for natural color images which is based on double level Reversible Color Transform(RCT) and Bi-level Burrows Wheeler Compression Algorithm(BBWCA) is proposed. The first level RCT from RGB to HSV provide higher reduction in number of unique hue components, thus gives higher compression in case of natural images. The first level RCT followed by the second level RCT from HSV to YUV helps to yield small number of unique Y component values. DC level shifting and twos complement operation will be applied as preprocessing steps to second level RCT. The result of double level RCT is used as the input to BBWCA, in which row-wise BWT followed by column-wise BWT is applied. The compressed image data is formed by using move-to-front(MTF), Run-length-encoding and Entropy coding. The proposed method using double level RCT with BBWCA results in high compression by taking advantage of reduction in hue components of natural images. Among the different color space compared, the proposed method achieves better compression and is well suited for small and large size natural images. Proposed method make use of a double level RCT on the existing BBWCA algorithm and resulted in improving the compression ratio by 46 percentage.

Efficient Lossless Image Compression using Modified Hierarchical Forecast and Context Adaptive System

Objective: The main intention of this research is to protect the sharpness of image through decreasing the bit rates in compression image. The proposed approach also intends to improve the compression ratio by predicting the diagonal pixels along with the horizontal and vertical pixels of the image. Methods: In this research, Modified Hierarchical Prediction and Context Adaptive (MHPCA) coding is introduced to overcome the issue of enormous prediction error rate near edges and preserves the sharpness of images. MHPCA coding scheme considers the vertical, horizontal and diagonal (left up, left down and right up, right down) predictors to predict pixels of the image. The inclusion of diagonal predictor enhances the prediction accuracy of pixels in hierarchical prediction. Findings: The experimental tests of MHPCA are conducted and the performance is compared with the existing HPCA scheme which proves that the proposed methodology can preserve the sharpness of the image in an efficient manner than HPCA method. The comparison results prove that the proposed methodology is also efficient in terms of performance metrics such as Bits per Pixel (BPP), Compression Ratio, Mean Square Error rate (MSE) and Peak Signal to Noise Ratio (PSNR). Application/Improvement: The findings demonstrate that by using proposed MHPCA scheme the bit rates in compressed images are reduced significantly which preserves the sharpness of the image. MHPCA coding scheme also reduces the error rate considerably and produces higher compression ratio than the existing HPCA scheme. Keywords: Context Adaptive Coding, Lossless Color Image Compression, Modified Hierarchical Prediction

A LOSSLESS COLOR IMAGE COMPRESSION USING AN IMPROVED REVERSIBLE COLOR TRANSFORM WITH HIERARCHICAL PREDICTION AND MODIFIED HUFFMAN CODING

In case of the conventional lossless color image compression methods, the pixels are interleaved from each color component, and they are predicted and finally encoded. In this paper, we propose a lossless color image compression method using hierarchical prediction of chrominance channel pixels and encoded with modified Huffman coding. An input image is chosen and the R, G and B color channel is transform into YCuCv color space using an improved reversible color transform. After that a conventional lossless image coder like CALIC is used to compress the luminance channel Y. The chrominance channel Cu and Cv are encoded with hierarchical decomposition and directional prediction. The effective context modeling for prediction residual is adopted finally. It is seen from the experimental result the proposed method improves the compression performance than the existing method.

English

Lossless image compression is a class of image compression algorithms that allows the original image to be perfectly reconstructed from the compressed image. This undertaking shows another lossless color image compression algorithm based on pixel prediction and arithmetic coding. Lossless squeezing of a Red, Green and Blue (RGB) picture is carried out by first decorrelating utilizing Reversible Color Transform (RCT). The got Y part is then compacted by a conventional lossless grayscale picture clamping strategy. The chrominance image is encoded utilizing Arithmetic coding and pixel prediction system. By utilizing RCT the forecast lapse is characterized and arithmetic coding is connected to the mistake signal. The packed image and encoded picture is joined to structure a lossless compacted RGB picture. It is demonstrated that this system diminishes the bit rates contrasted and JPEG 2000 and JPEG-XR. With a specific end goal to further lessen the bit rate the compression strategies and the pixel prediction strategy can be altered for better execution. Keywords-Lossless image compression, Pixel Prediction, Arithmetic Coding, RCT, Huffman coding

Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression

This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels. An appropriate context model for the prediction error is also defined and the arithmetic coding is applied to the error signal corresponding to each context. For several sets of images, it is shown that the proposed method further reduces the bit rates compared with JPEG2000 and JPEG-XR.

Adaptive visually lossless JPEG-based color image compression

The paper presents two approaches to adaptive JPEG-based compression of color images inside digital cameras. Compression for both approaches, although lossy, is organized in such a manner that introduced distortions are not visible. This is done taking into account quality of each original image before it is subject to lossy compression. Noise characteristics and blur are assumed to be the main factors determining visual quality of original images. They are estimated in a fast and blind (automatic) manner for images in RAW format (first approach) and in Bitmap (second approach). The dominant distorting factor which can be either noise or blur is determined. Then, the scaling factor (SF) of JPEG quantization table is adaptively adjusted to preserve valuable information in a compressed image with taking into account estimated noise and blur influence. The advantages and drawbacks of the proposed approaches are discussed. Both approaches are intensively tested for real-life images. It is demonstrated that the second approach provides more accurate estimate of degrading factor characteristics, and thus, a larger compression ratio (CR) increase compared to super-high quality (SHQ) mode used in consumer digital cameras. The first approach mainly relies on the prediction of noise and blur characteristics to be observed in Bitmap images after a set of nonlinear operations applied to RAW data in image processing chain. It is simpler and requires less memory but appeared to be slightly less beneficial. Both approaches are shown to provide, on the average, more than two times increase in average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images. This is proven by the analysis of modern visual quality metrics able to adequately characterize compressed image quality.

채널 간 상관관계를 이용한 무손실 컬러 이미지 압축

기존의 무손실 압축은 일반적으로 채널간의 상관관계를 고려하지 않고 각각의 채널을 따로 압축하였다. 채널 간 상관관계를 이용하는 경우도 있으나 이들은 위성/항공 영상과 같은 경우에 제한된 것으로서 일반적인 이미지에 대해서는 만족할만한 성능을 얻지 못하였다. 본 논문에서는 컬러 이미지가 갖는 채널 간의 상관관계를 이용하여 무손실 압축의 성능을 향상시키는 방법을 제안하였다. 제안된 방법에서는 채널 간 상관관계를 표현할 수 있도록 하기 위하여, 비대칭 샘플링을 통해 주어진 영상의 모자이크 이미지를 구성하여 압축을 수행하였고, 압축된 모자이크 이미지의 정보를 이용해서 샘플링되지 않은 나머지 이미지를 예측하여 압축할 정보의 양을 감소시켰다. 제안된 방법은 압축 성능을 평가하는데 일반적으로 사용되는 데이터들에 대해서 채널 간 상관관계를 이용하지 않는 방법에 대해 35%, 채널 간 상관관계를 이용한 기존 방법에 대해 10%의 성능 개선을 보인다. The conventional lossless compression of color images is to apply a compression method to each of color components separately, without considering the channel correlation. There had been several methods that consider the channel correlation, but they were confined to the compression of satellite or aerial images only, and the performance of these algorithms to general photos is not satisfactory. This paper proposes a new lossless color image compression method that exploits the correlation between the color components. Specifically, asymmetric sampling is applied to transform an image into mosaic image and the rest, which are compressed separately. By using the information from the compressed mosaic image, the rest images are predicted for further reducing the information to be compressed. Experimental results show that the proposed method improves the compression performance by 35% over the conventional separate compression methods and 10% over the existing methods that exploit the channel correlation.

A Lossless Color Image Compression Architecture Using a Parallel Golomb-Rice Hardware CODEC

In this paper, a high performance lossless color image compression and decompression architecture to reduce both memory requirement and bandwidth is proposed. The proposed architecture consists of differential-differential pulse coded modulation (DDPCM) and Golomb-Rice coding. The original image frame is organized as m by n sub-window arrays, to which DDPCM is applied to produce one seed and m × n - 1 pieces of differential data. Then the differential data are encoded using the Golomb-Rice algorithm to produce losslessly compressed data. According to the experimental results on benchmark images, the proposed architecture can guarantee high enough compression rate and throughput to perform real-time lossless CODEC operations with a reasonable hardware area.