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.     

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 video compression based on backward adaptive pixel-based fast motion estimation

This paper presents a lossless video compression system based on a novel Backward Adaptive pixel-based fast Predictive Motion Estimation (BAPME). Unlike the widely used block-matching motion estimation techniques, this scheme predicts the motion on a pixel-by-pixel basis by comparing a group of past observed pixels between two adjacent frames, eliminating the need of transmitting side information. Combined with prediction and a fast search technique, the proposed algorithm achieves better entropy results and significant reduction in computation than pixel-based full search for a set of standard test sequences. Experimental results also show that BAPME outperforms block-based full search in terms of speed and entropy. We also provide the sub-pixel version of BAPME as well as integrate BAPME in a complete lossless video compression system. The experimental results are superior to the selected state-of-the-art schemes.     

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 image coding via adaptive linear prediction andclassification

In past years, there have been several improvements in lossless image compression. All the recently proposed state-of-the-art lossless image compressors can be roughly divided into two categories: single and double-pass compressors. Linear prediction is rarely used in the first category, while TMW, a state-of-the-art double-pass image compressor, relies on linear prediction for its performance. We propose a single-pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. Locality is also exploited in the entropy coding of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our ALPC obtains a compression ratio comparable to CALIC while improving on some images     

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 image compression using ordered binary-decision diagrams

A lossless compression algorithm for images based on ordered binary-decision diagrams (OBDDs) is presented. The algorithm finds an OBDD which represents the image exactly and then codes the OBDD efficiently. The results obtained show a great improvement with respect to a previous work     

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 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.     

一种适用于VTS系统的自适应雷达视频压缩方法

提出了一种适用于VTS系统的自适应的雷达视频压缩方法,介绍了该压缩方法的原理以及如何在VTS系统中使用该方法以提高雷达视频压缩效率。     

Fractal coding of video sequence using circular prediction mappingand noncontractive interframe mapping

We propose a novel algorithm for fractal video sequence coding, based on the circular prediction mapping and the noncontractive interframe mapping. The proposed algorithm can effectively exploit the temporal correlation in real image sequences, since each range block is approximated by the domain block in the adjacent frame, which is of the same size as the range block. The computer simulation results demonstrate that the proposed algorithm provides very promising performance at low bit rate, ranging from 40-250 kbyte/s.     

Lossless compression of color mosaic images.

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.     

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.     

ECG compression using long-term prediction

A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The periodicity of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent RMS difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction-STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method     

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 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 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.     

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     

Motion compensation based on tangent distance prediction for video compression

We present a new algorithm for motion compensation that uses a motion estimation method based on tangent distance. The method is compared with a Block-Matching based approach in various common situations. Whereas Block-Matching algorithms usually only predict positions of blocks over time, our method also predicts the evolution of pixels into these blocks. The prediction error is then drastically decreased. The method is implemented into the Theora codec proving that this algorithm improves the video codec performances.     

Lossless image compression based on optimal prediction, adaptivelifting, and conditional arithmetic coding

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.