LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.

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The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.

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Conditional Probability Models for Deep Image Compression

This paper sheds light on the least-square (LS)-based adaptive prediction schemes for lossless compression of natural images. Our analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly around the edge areas, we propose a novel approach to reduce its computational complexity with negligible performance sacrifice. The lossless image coder built upon the new prediction scheme has achieved noticeably better performance than the state-of-the-art coder CALIC with moderately increased computational complexity.

Edge-directed prediction for lossless compression of natural images

We present a general purpose lossless greyscale image compression method TMW that is based on the use of linear predictors and implicit seg mentation In order to achieve competitive com pression the compression process is split into an analysis step and a coding step In the rst step a set of linear predictors and other parameters suitable for the image is calculated which is in cluded in the compressed le and subsequently used for the coding step This adaption allows TMW to perform well over a very wide range of im age types Other signi cant features of TMW are the use of a one parameter probability distribu tion probability calculations based on unquantized prediction values blending of multiple probability distributions instead of prediction values and im plicit image segmentation The method has applications beyond image com pression The work is also relevant to image seg mentation and image comparison For image compression the method has been compared to CALIC on a selection of test images and typically outperforms it by between and percent at the cost of considerably slower com pression In particular a bitrate of less than bpp has been achieved for the luminance band of the well known lenna image compared to bpp reported for CALIC in Wu

TMW - a new method for lossless image compression

Summary form only given. In differential pulse code modulation (DPCM) we make a prediction f/spl circ/=/spl Sigma/a(i)-f(i) of the next pixel using a linear combination of neighbouring pixels f(i). It is possible to have the coefficients a(i)s constant over a whole image, but better results can be obtained by adapting the a(i)s to the local image behaviour as the image is encoded. One difficulty with present schemes is that they can only produce predictors with positive a(i)s. This is desirable in the presence of noise, but in regions where the intensity varies smoothly, we require at least one negative coefficient to properly estimate a gradient. However, if we consider the four neighbouring pixels as four local sub-predictors W, N, NW and NE, and the gradient measure as the sum of absolute prediction errors of those sub-predictors within the local neighbourhood, then we can use any sub-predictors we choose, even nonlinear ones. In our experiments, we chose to use three additional linear predictors suited for smooth regions, each having one negative coefficient. Results were computed for three versions of the standard JPEG test set and some 12 bpp medical images.

Generalised locally adaptive DPCM

History-based blending of image sub-predictors

The trend in modern image noise filtering algorithms has been toward structure preservation by using only those neighbouring pixels which are similar to the current pixel in some way. In this paper we introduce a technique, call FUELS (filtering using explicit local segmentation), which explicitly segments the m /spl times/ m region encompassing the current pixel and filters using only those pixels from the same segment. By exploiting mask overlap an effective mask size of(2m-1)/spl times/(2m-1) is obtained, as well as robustness in regions which do not fit the image model. The algorithm can be iterated, and our results show FUELS to outperform existing algorithms both quantitatively and qualitatively.

Peter E Tischer

Papers

TMW - a new method for lossless image compression

Generalised locally adaptive DPCM

History-based blending of image sub-predictors

Structure preserving noise filtering of images using explicit local segmentation

Generalised locally adaptive dpcm (abstract)