New simple and efficient color space transformations for lossless image compression

We present simple color space transformations for lossless image compression and compare them with established transformations including RCT, YCoCg-R and with the optimal KLT for 3 sets of test images and for significantly different compression algorithms: JPEG-LS, JPEG2000 and JPEG XR. One of the transformations, RDgDb, which requires just 2 integer subtractions per image pixel, on average results in the best ratios for JPEG2000 and JPEG XR, while for a specific set or in case of JPEG-LS its compression ratios are either the best or within 0.1 bpp from the best. The overall best ratios were obtained with JPEG-LS and the modular-arithmetic variant of RDgDb (mRDgDb). Another transformation (LDgEb), based on analog transformations in human vision system, is with respect to complexity and average ratios better than RCT and YCoCg-R, although worse than RDgDb; for one of the sets it obtains the best ratios.     

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.     

A lossless compression algorithm of remote sensing image for space applications

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.     

Parallel color space converters for JPEG image compression

This paper proposes the design of parallel and pipelined architectures to the first step of the JPEG compression: the color space conversion from RGB to YCbCr. The design of these parallel architectures uses a non-parallel architecture designed in previous works. The parallelism makes possible a great gain in terms of pixels processing rate, with a very small impact in terms of resources usage. The proposed architectures were described in VHDL and synthesized for Altera Flex 10KE FPGAs family, reaching frequencies near to 40 MHz. The parallel architectures reach a processing rate higher than 10 million pixels per second. These architectures are able to convert the color space of more than 40 color images of 640 × 480 pixels at each second.     

Gray-level-embedded lossless image compression

A level-embedded lossless compression method for continuous-tone still images is presented. Level (bit-plane) scalability is achieved by separating the image into two layers before compression and excellent compression performance is obtained by exploiting both spatial and inter-level correlations. A comparison of the proposed scheme with a number of scalable and non-scalable lossless image compression algorithms is performed to benchmark its performance. The results indicate that the level-embedded compression incurs only a small penalty in compression efficiency over non-scalable lossless compression, while offering the significant benefit of level-scalability.     

On the use of standards for microarray lossless image compression

The interest in methods that are able to efficiently compress microarray images is relatively new. This is not surprising, since the appearance and fast growth of the technology responsible for producing these images is also quite recent. In this paper, we present a set of compression results obtained with 49 publicly available images, using three image coding standards: lossless JPEG2000, JBIG, and JPEG-LS. We concluded that the compression technology behind JBIG seems to be the one that offers the best combination of compression efficiency and flexibility for microarray image compression.     

Adaptive prediction for lossless image compression

Lossless image compression is often performed through decorrelation, context modelling and entropy coding of the prediction error. This paper aims to identify the potential improvements to compression performance through improved decorrelation. Two adaptive prediction schemes are presented that aim to provide the highest possible decorrelation of the prediction error data. Consequently, complexity is overlooked and a high degree of adaptivity is sought. The adaptation of the respective predictor coefficients is based on training of the predictors in a local causal area adjacent to the pixel to be predicted. The causal nature of the training means no transmission overhead is required and also enables lossless coding of the images.The first scheme is an adaptive neural network, trained on the actual data being coded enabling continuous updates of the network weights. This results in a highly adaptive predictor, with localised optimisation based on stochastic gradient learning. Training for the second scheme is based on the recursive LMS (RLMS) algorithm incorporating feedback of the prediction error. In addition to the adaptive prediction, the results presented here also incorporate an arithmetic coding scheme, producing results which are better than CALIC.     

Lossless compression of HDR color filter array image for the digital camera pipeline

This paper introduces a lossless color filter array (CFA) image compression scheme capable of handling high dynamic range (HDR) representation. The proposed pipeline consists of a series of pre-processing operations followed by a JPEG XR encoding module. A deinterleaving step separates the CFA image to sub-images of a single color channel, and each sub-image is processed by a proposed weighted template matching prediction. The utilized JPEG XR codec allows the compression of HDR data at low computational cost. Extensive experimentation is performed using sample test HDR images to validate performance and the proposed pipeline outperforms existing lossless CFA compression solutions in terms of compression efficiency.     

A chip set for lossless image compression

The authors describe two chips which form the basis of a high-speed lossless image compression/decompression system. They present the transform and coding algorithms and the main architectural features of the chips and outline some performance specifications. Lossless compression can be achieved by a transformation process followed by entropy coding. The two application-specific integrated circuits (ASICs) perform S-transform image decomposition and the Lempel-Ziv (L-Z) type of entropy coding. The S-transform, besides decorrelating the image, provides a convenient method of hierarchical image decomposition. The data compressor/decompressor IC is a fast and efficient implementation of the L-Z algorithm. The chips can be used independently or together for image compression     

A set of transformations for lossless image compression

Based on the property of binary arithmetic operations and the spatial probability distribution of the prediction error, a set of transformations is proposed for the lossless image compression. The bit plane coding technique is used in the transformed image. The transformations are easily implementable. Experimental results show that these transformations are useful.     

On the use of context-weighting in lossless bilevel image compression.

We present a context-weighting algorithm that adaptively weights in real-time three-context models based on their relative accuracy. It can automatically select the better model over different regions of an image, producing better probability estimates than using either one of these models exclusively. Combined with the previously proposed block arithmetic coder for image compression (BACIC), the overall performance is slightly better than JBIG for the eight CCITT business-type test images, outperforms JBIG by 13.8% on halftone images, and by 17.5% for compounded images containing both text and halftones. Furthermore, users no longer need to select models as in JBIG and BACIC to get the better performance.     

Dictionary-based histogram packing technique for lossless image compression

This paper proposes a dictionary-based histogram packing technique for lossless image compression. It is used to improve the performance of the state-of-the-art lossless image compression standards and methods when compressing sparse and locally sparse histogram images. The proposed method leverages inter-block correlations and similarities not only within the neighborhood but also across the entire image, thereby effectively reducing the block boundary artifacts commonly observed in block-based histogram packing techniques. To achieve this, a dictionary is employed to represent highly correlated blocks using a key that captures the union of their active symbol sets. Experimental results have demonstrated that the proposed method, when applied to sparse and locally sparse histogram images, enhances the performance of various state-of-the-art lossless image compression techniques. Notably, improvements were observed in standards and methods such as JPEG-2000, JPEG-LS, JPEG-XL, PNG, and CALIC.     

Context-dependent multilevel pattern matching for lossless image compression

In this paper, the multilevel pattern matching (MPM) code for compression of one-dimensional (1D) sequences is first generalized to compress two-dimensional (2D) images, resulting in a 2D multilevel pattern matching (MPM) code. It is shown that among all images of n pixels, the worst case redundancy of the 2D MPM code against any finite-template-based arithmetic code is O(1//spl radic/logn). This result contrasts unfavorably with the fact that among all 1D sequences of length n, the MPM code has a worst case redundancy of O(1/logn) against any finite-state arithmetic code; this is caused by the so-called 2D boundary effect. To alleviate the 2D boundary effect, we extend the 2D MPM code to the case of context modeling, yielding a context-dependent 2D MPM code. It is shown that among all images of n pixels, the context-dependent 2D MPM code has an O(1/logn) worst case redundancy against any finite-template-based arithmetic code satisfying a mild condition; this redundancy is better than that of the 2D MPM code without context models. Experimental results demonstrate that the context-dependent 2D MPM code significantly outperforms the 2D MPM code without context models for bi-level images. It is also demonstrated that, in terms of compression rates, the context-dependent 2D MPM code performs significantly better than the progressive coding mode of JBIG1 for both textual and bi-level images, and better than or comparably to the sequential coding mode of JBIG1 and JBIG2. In addition to its excellent compression performance, the context-dependent 2D MPM code allows progressive transmission of images.     

Four-dimensional matrix Walsh transform for lossless compression of color video

This article presents a coding method for the lossless compression of color video.In the proposed method,four-dimensional matrix Walsh transform(4D-M-Walsh-T)is used for color video coding.The whole n frames of a color video sequence are divided into '3D-blocks' which are image width(row component),image height(column component),image width(vertical component)in a color video sequence,and adjacency(depth component)of n frames(Y,U or V)of the video sequence.Similar to the method of 2D-Walsh transform,4D-M-Walsh-T is 4D sub-matrices,and the size of each sub-matrix is n.The method can fully utilize correlations to encode for lossless compression and reduce the redundancy of color video,such as adjacent pixels in one frame or different frames of a video at the same time.Experimental results show that the proposed method can achieve higher lossless compression ratio(CR)for the color video sequence.     

靶场光测数字图像无损压缩技术方案设计

为了解决困扰靶场的海量光学测量图像信息存储和传输问题,在满足光测数据处理精度前提下实现较高的压缩比,根据靶场光测数字图像特点,采用H.264的编码方案对目标区域和背景区域进行编码,得到了70倍压缩图像数据和压缩前后目标在图像坐标系中的坐标结果。结果表明,光测数字图像无损压缩技术实现了背景区域的高倍压缩和对目标区域的无损压缩,从而既满足了靶场数据处理精度需求又大大减少了测量图像信息量,该技术可对标准位图图像进行处理,符合国内各靶场的测量图像标准,可在其它靶场广泛推广。     

Open-loop rate control for JPEC-LS near lossless image compression

A low cost rate control scheme is presented to achieve an open-loop rate control for JPEG-LS near lossless coding mode, the forthcoming new standard for lossless image compression. The scheme is implemented by further exploitation of the local texture analysis, designed in JPEG-LS, to indicate the compressibility indirectly. Hence, the information loss level may be controlled automatically and can be adaptive to the local texture. Consequently, a similar compression ratio is maintained for different images     

Novel adaptive color space transform and application to image compression

Various contemporary standards by Joint Picture Expert Group, which is used for compression, exploited the correlation among the color components using a component color space transform before the subband transform stage. The transforms used to de-correlate the colors are primarily the fixed kernel transforms, which are not suitable for large class of images. In this paper an image dependent color space transform (ID-CCT), exploiting the inter-channel redundancy optimally and which is very much suitable for compression has been proposed. Also the comparative performance has been evaluated and a significant improvement has been observed, objectively as well as subjectively over other quantifiable methods.     

高动态范围数字相机sRGB色彩空间颜色管理

数字成像设备在传输、显示图像时需要进行设备的特性化处理,该过程是设备颜色管理中的重要环节。为了保证输出图像的色彩复现能力,高动态范围数字相机需要根据设备自身特性进行特性化。现有特性化过程一般针对8位数字相机,并且转换矩阵的标定较为复杂,容易产生系统误差,矩阵精度易受影响。针对上述问题,提出了一种高动态范围彩色数字相机颜色管理方法,利用相机输出图像与被摄目标的色彩属性建立了RGB色彩空间到CIE1931 XYZ色彩空间的映射关系,并采用最小二乘拟合法对映射矩阵进行标定,最终将相机的RGB色彩空间转换至s RGB标准色彩空间,解决了高动态相机的特性化问题。实验证明,该方法操作简单,设备通用性较强,拟合均方误差优于0.08,具有较好的鲁棒性。     

Local color oppugnant quantized extrema patterns for image retrieval

Multidimensional Systems and Signal Processing - A new image feature vector named, local color oppugnant quantized extrema pattern (LCOQEP) is proffered in this paper. In the proposed method,...     

On independent color space transformations for the compression ofCMYK images

Device and image-independent color space transformations for the compression of CMYK images were studied. A new transformation (to a YYCC color space) was developed and compared to known ones. Several tests were conducted leading to interesting conclusions. Among them, color transformations are not always advantageous over independent compression of CMYK color planes. Another interesting conclusion is that chrominance subsampling is rarely advantageous in this context. Also, it is shown that transformation to YYCC consistently outperforms the transformation to YCbCrK, while being competitive with the image-dependent KLT-based approach.