Context-based compression of binary images in parallel

Mathematics in Computer Science, 2010

Approximation schemes for optimal compression with static and sliding dictionaries which can run on a simple array of processors with distributed memory and no interconnections are presented. These approximation algorithms can be implemented on both small and large scale parallel systems. The sliding dictionary method requires large size files on large scale systems. As far as lossless image compression is concerned, arithmetic encoders enable the best lossless compressors but they are often ruled out because they are too complex. Storer extended dictionary text compression to bi-level images to avoid arithmetic encoders (BLOCK MATCHING). We were able to partition an image into up to a hundred areas and to apply the BLOCK MATCHING heuristic independently to each area with no loss of compression effectiveness. Therefore, the approach is suitable for a small scale parallel system at no communication cost. On the other hand, bi-level image compression seems to require communication on large scale systems. With regard to grey scale and color images, parallelizable lossless image compression (PALIC) is a highly parallelizable and scalable lossless compressor since it is applied independently to blocks of 8 × 8 pixels. We experimented the BLOCK MATCHING and PALIC heuristics with up to 32 processors of a 256 Intel Xeon 3.06 GHz processors machine (http://avogadro.cilea.it) on a test set of large topographic bi-level images and color images in RGB format. We obtained the expected speed-up of the compression and decompression times, achieving parallel running times about 25 times faster than the sequential ones. Finally, scalable algorithms computing static and sliding dictionary optimal text compression on an exclusive read, exclusive write shared memory parallel machine are presented. On the same model, compression by block matching of bi-level images is shown which can be implemented on a full binary tree architecture under some realistic assumptions with no scalability issues.

Journal of Electronic Imaging, 2006

Color separation and highly optimized context tree modeling for binary layers have provided the best compression results for color map images that consist of highly complex spatial structures but only a relatively few number of colors. We explore whether this kind of approach works on photographic and palette images as well. The main difficulty is that these images can have a much higher number of colors, and it is therefore much more difficult to exploit spatial dependencies via binary layers. The original contributions of this work include: 1. the application of context-tree-based compression (previously designed for map images) to natural and color palette images; 2. the consideration of four different methods for bit-plane separation; and 3. Extension of the two-layer context to a multilayer context for better utilization of the crosslayer correlations. The proposed combination is extensively compared to state of the art lossless image compression methods.

International Journal of Computer …, 2011

Modern data processing tasks involving high computation with huge data intensive work are not providing any usual response as they run over a conventional computing architecture, where the synergism capabilities of such machines are limited to single ...

Proceedings - International Conference on Image Processing, ICIP, 2005

Recently, Chen et al. proposed a method for compressing color-quantized images that is based on a binary tree representation of colors and on context-based arithmetic coding with variable size templates. In this paper, we address the problem of context adaptation and we propose a model that provides improvements in the lossless compression capabilities of Chen's method.

Precise estimation of the probabilistic structure of natural images, i.e., entropy modeling, plays an essential role for both lossless and lossy image compression. Despite remarkable success of recent end-to-end optimized image compression, the latent code representation is usually assumed to be fully statistically factorized to simplify entropy modeling. However, such assumption generally does not hold true and is unbeneficial to compression performance. In this work, we present context-based convolutional networks (CCNs) that exploit statistical redundancies in the codes for effective entropy modeling. In particular, a 3D zigzag coding order as well as a 3D code dividing technique are respectively introduced to define proper context and to achieve parallel entropy decoding for efficiency, both of which boil down to place translation-invariant binary masks on convolution filters of CCNs. We demonstrate the power of CCNs for entropy modeling in both lossless and lossy image compression. For the former, we directly apply a CCN to binarized plane representation of an image for estimating the Bernoulli distribution of each code. For the latter, the categorical distribution of each code is represented by a discretized mixture of Gaussian distributions, whose parameters are estimated by three CCNs. And the CCN-based entropy model can be jointly learned with analysis and synthesis transforms for optimized rate-distortion performance. Experiments on the Kodak and Tecnick datasets show that the proposed lossless and lossy image compression methods based on CCNs generally achieve better compression performance than existing methods with a manageable computational complexity.

The Journal of Supercomputing, 2018

Today, there is a huge demand for data compression due to the need to reduce the transmission time and increase the capacity of data storage. Data compression is a technique which represents an information, images, video files in a compressed or in a compact format. There are various data compression techniques which keep information as accurately as possible with the fewest number of bits and send it through communication channel. Arithmetic algorithm, Lempel-Ziv 77 (LZ77) and run length encoding with a K-precision (K-RLE) algorithms are lossless data compression algorithms which have lower performance rate because of their processing complexity as well as execution time. This paper presents an efficient parallel approach to reduce execution time for compression algorithms. The proposed OpenMP is an efficient tool for programming within parallel shared-memory environments. Finally, it shows that performance parallel model experimented using Open Multi-Processing (OpenMP) Application Programming Interface through Intel Parallel studio on multicore architecture platform with spec of Core 2 duo-2.4 GHz, 1 Gb RAM machine of parallel approach for compression algorithms has been improved remarkably against sequential approach. The improvement in compression ratio through an efficient parallel approach leads to reduction on transmission cost, reduction in storage space and bandwidth without additional hardware infrastructure. An overall performance evaluation shows arithmetic data compression algorithm with 46% which is better than LZ77 of 44% as well as K-RLE of 37% data compression algorithms.

Journal 4 Research - J4R Journal, 2016

Image compression technique is used in many applications for example, satellite imaging, medical imaging, video where the size of the iamge requires more space to store, in such application image compression effectively can be used. There are two types in image compression techniques Lossy and Lossless comression. Both these techniques are used for compression of images, but these techniques are not fast. The image compression techniques both lossy and lossless image compression techniques are not fast, they take more time for compression and decompression. For fast and efficient image compression a parallel computing technique is used in matlab. Matlab is used in this project for parallel computing of images. In this paper we will discuss Regular image compression technique, three alternatives of parallel computing using matlab, comparison of image compression with and without parallel computing.

IEEE Transactions on Image Processing

Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized image compression, the latent codes are usually assumed to be fully statistically factorized in order to simplify entropy modeling. However, this assumption generally does not hold true and may hinder compression performance. Here we present contextbased convolutional networks (CCNs) for efficient and effective entropy modeling. In particular, a 3D zigzag scanning order and a 3D code dividing technique are introduced to define proper coding contexts for parallel entropy decoding, both of which boil down to place translation-invariant binary masks on convolution filters of CCNs. We demonstrate the promise of CCNs for entropy modeling in both lossless and lossy image compression. For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation. For the latter, the categorical distribution of each code is represented by a discretized mixture of Gaussian distributions, whose parameters are estimated by three CCNs. We then jointly optimize the CCNbased entropy model along with analysis and synthesis transforms for rate-distortion performance. Experiments on the Kodak and Tecnick datasets show that our methods powered by the proposed CCNs generally achieve comparable compression performance to the state-of-the-art while being much faster.

2011

The proliferation of pictures and videos in the Internet is imposing heavy demands on mobile data networks. This demand is expected to grow rapidly and a one-fit-all solution is unforeseeable. While researchers are approaching the problem from different directions, we identify a human-centric opportunity to reduce content size. Our intuition is that humans exhibit unequal interest towards different parts of a content, and parts that are less important may be traded off for price/performance benefits. For instance, a picture with the Statue of Liberty against a blue sky may be partitioned into two categories -the semantically important statue, and the less important blue sky. When the need to minimize bandwidth/energy is acute, only the picture of the statue may be downloaded, along with a meta tag "background: blue sky". Once downloaded, an arbitrary "blue sky" may be suitably inserted behind the statue, reconstructing an approximation of the original picture. As long as the essence of the picture is retained from the human's perspective, such an approximation may be acceptable. This paper attempts to explore the scope and usefulness of this idea, and develop a broader research theme that we call context-aware compression.

1996

LOCO-I (LOw COmplexity LOssless COmpression for Images) is a novel lossless compression algorithm for continuous-tone images which combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.