Lossless image compression using adjustable fractional line-buffer

Video frame memory compression has gained increased popularity in video processing ICs to save external memory storage size and reduce memory access bandwidth. This technique is especially important in portable devices where efficient use of energy is critical for the deployment of video applications. In this paper, we propose a low-complexity lossless image compression method that uses only a fraction of one line-buffer. The proposed method first employs integer wavelet transform (IWT), and then low-frequency coefficients prediction of each segment based on those from the segment in the line above, and last Golomb-Rice (GR) encoding to achieve low-cost and highly efficient compression. Simulation results demonstrate that the proposed method gives a compression ratio comparable with the existing state-of-the-art low-complexity methods while significantly lowering the internal memory cost and keeping the complexity low.     

Lossless compression of multispectral image data

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. The authors present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a noncausal prediction model for an image. The authors present a backward adaptive technique and a forward adaptive technique. They then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal     

Lossless compression of VLSI layout image data.

We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm can be applied to many lossless compression applications, such as document image compression, our primary target application has been lossless compression of integrated circuit layout image data. These images contain a heterogeneous mix of data: dense repetitive data better suited to LZ-style coding, and less dense structured data, better suited to context-based encoding. As part of C4, we have developed a novel binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding, and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and two-dimensional LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for gray-pixel image data.     

Lossless image compression with a codebook of block scans

When applying predictive compression on image data there is an implicit assumption that the image is scanned in a particular order. Clearly, depending on the image, a different scanning order may give better compression. In earlier work, we had defined the notion of a prediction tree (or scan) which defines a scanning order for an image. An image can be decorrelated by taking differences among adjacent pixels along any traversal of a scan. Given an image, an optimal scan that minimizes the absolute sum of the differences encountered can be computed efficiently. However, the number of bits required to encode an optimal scan turns out to be prohibitive for most applications. In this paper we present a prediction scheme that partitions an image into blocks and for each block selects a scan from a codebook of scans such that the resulting prediction error is minimized. Techniques based on clustering are developed for the design of a codebook of scans. Design of both semiadaptive and adaptive codebooks is considered. We also combine the new prediction scheme with an effective error modeling scheme. Implementation results are then given, which compare very favorably with the JPEG lossless compression standard     

Lossless compression of seismic signals using differentiation

For some classes of signals, particularly those dominated by low frequency components, such as seismic data first and higher order differences between adjacent signal samples are generally smaller compared with the signal samples. In this paper, evaluating the differencing approach for losslessly compressing several classes of seismic signals is given. Three different approaches employing derivatives are developed and applied. The performance of the techniques presented and the adaptive linear predictor are evaluated and compared for the lossless compression of different seismic signal classes. The proposed differentiator approach yields comparable residual energy compared with that obtained employing the linear predictor technique. The two main advantages of the differentiation method are: (1) the coefficients are fixed integers which do not have to be encoded; and (2) greatly reduced computational complexity, relative to the existing algorithms. These advantages are particularly attractive for real time processing. They have been confirmed experimentally by compressing different seismic signals. Sample results including the compression ratio, i.e., the ratio of the number of bits per sample without compression to those with compression using arithmetically encoded residues are also given     

Lossless compression of video using temporal information

We consider the problem of lossless compression of video by taking into account temporal information. Video lossless compression is an interesting possibility in the line of production and contribution. We propose a compression technique which is based on motion compensation, optimal three-dimensional (3-D) linear prediction and context based Golomb-Rice (1966, 1979) entropy coding. The proposed technique is compared with 3-D extensions of the JPEG-LS standard for still image compression. A compression gain of about 0.8 bit/pel with respect to static JPEG-LS, applied on a frame-by-frame basis, is achievable at a reasonable computational complexity.     

基于FPGA的无损图像压缩系统设计

本文简要介绍了图像压缩的重要性和常用的无损图像压缩算法,分析了快速高效无损图像压缩算法(FELICS)的优势,随后详细分析了该算法的编码步骤和硬件实现方案,最后公布了基于该方案的FPGA性能指标。和其他压缩算法相比该方案可极大地减小无损图像压缩系统所需的存储空间和压缩时间。     

Lossless video sequence compression using adaptive prediction.

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.     

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.     

Lossless image compression by quantization feedback in acontent-driven enhanced Laplacian pyramid

In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost.     

基于预测与JPEG2000的高光谱图像无损压缩方法

随着成像光谱仪向着高光谱分辨率、高空间分辨率方向发展,高光谱图像的数据量呈几何级数增长。由于数据传输和存储能力的限制,必须对高光谱图像进行有效压缩。首先,对高光谱图像的相关性进行了深入分析,得知其具有一定的空间相关性和极强的谱间相关性,从而具有较强的可压缩性。其次,结合JPEG2000对DPCM进行了修改,提出了基于一阶线性预测与JPEG2000相结合的无损压缩方案。最后,在软件平台上实现了该方案,并取得了较好的压缩效果。结果表明,该方案可以有效的实现高光谱图像无损压缩,验证了方案的可行性,为硬件平台上实现该方案提供了理论依据。     

机器学习理论的超光谱遥感图像无损压缩

针对一般超光谱遥感图像的压缩方法无法同时实现图像信息缩减和图像完整性的问题,提出一种机器学习理论的超光谱遥感图像无损压缩方法.利用机器学习中的聚类算法进行第一次压缩,减少超光谱遥感图像中的冗余波段光谱,并降低图像维度;再利用机器学习中的人工神经网络进行第二次压缩,将不同图像子块送入不同压缩率的神经元当中,通过隐含层自主...     

Lossless compression of color sequences using optimal linear prediction theory

In this paper, we present a novel technique that uses the optimal linear prediction theory to exploit all the existing redundancies in a color video sequence for lossless compression purposes. The main idea is to introduce the spatial, the spectral, and the temporal correlations in the autocorrelation matrix estimate. In this way, we calculate the cross correlations between adjacent frames and adjacent color components to improve the prediction, i.e., reduce the prediction error energy. The residual image is then coded using a context-based Golomb-Rice coder, where the error modeling is provided by a quantized version of the local prediction error variance. Experimental results show that the proposed algorithm achieves good compression ratios and it is roboust against the scene change problem.     

Lossless compression of medical images using two-dimensional multiplicative autoregressive models

Presents new methods for lossless predictive coding of medical images using two dimensional multiplicative autoregressive models. Both single-resolution and multi-resolution schemes are presented. The performances of the proposed schemes are compared with those of four existing techniques. The experimental results clearly indicate that the proposed schemes achieve higher compression compared to the lossless image coding techniques considered.     

Lossless compression of continuous-tone images

In this paper, we survey some of the recent advances in lossless compression of continuous-tone images. The modeling paradigms underlying the state-of-the-art algorithms, and the principles guiding their design, are discussed in a unified manner. The algorithms are described and experimentally compared     

Lossless compression of video sequences

We investigate lossless compression schemes for video sequences. A simple adaptive prediction scheme is presented that exploits temporal correlations or spectral correlations in addition to spatial correlations. It is seen that even with motion compensation, schemes that utilize only temporal correlations do not perform significantly better than schemes that utilize only spectral correlations. Hence, we look at hybrid schemes that make use of both spectral and temporal correlations. The hybrid schemes give significant improvement in performance over other techniques. Besides prediction schemes, we also look at some simple error modeling techniques that take into account prediction errors made in spectrally and/or temporally adjacent pixels in order to efficiently encode the prediction residual. Implementation results on standard test sequences indicate that significant improvements can be obtained by the proposed techniques     

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.     

Lossless compression of digital audio

Lossless audio compression is likely to play an important part in music distribution over the Internet, DVD audio, digital audio archiving, and mixing. The article is a survey and a classification of the current state-of-the-art lossless audio compression algorithms. This study finds that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio. It also describes a new lossless audio coder called AudioPak, which low algorithmic complexity and performs well or even better than most of the lossless audio coders that have been described in the literature     

Lossless compression of AVIRIS images

Adaptive DPCM methods using linear prediction are described for the lossless compression of hyperspectral (224-band) images recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The methods have two stages-predictive decorrelation (which produces residuals) and residual encoding. Good predictors are described, whose performance closely approaches limits imposed by sensor noise. It is imperative that these predictors make use of the high spectral correlations between bands. The residuals are encoded using variable-length coding (VLC) methods, and compression is improved by using eight codebooks whose design depends on the sensor's noise characteristics. Rice (1979) coding has also been evaluated; it loses 0.02-0.05 b/pixel compression compared with better VLC methods but is much simpler and faster. Results for compressing ten AVIRIS images are reported.