Context-based Adaptive Lossless Image Coding Research Articles

A Low-Complexity Embedded Compression Codec Design With Rate Control for High-Definition Video

A hardwired design of embedded compression engine targeting the reduction of full high-definition (HD) video transmission bandwidth over the wireless network is developed. It adopts an intra-coding framework and supports both lossless and rate-controlled near lossless compression options. The lossless compression algorithm is based on a simplified Context-Based, Adaptive, Lossless Image Coding (CALIC) scheme featuring pixelwise gradient-adjusted prediction and error-feedback mechanism. To reduce the implementation complexity, an adaptive Golomb-Rice coding scheme in conjunction with a context modeling technique is used in lieu of an adaptive arithmetic coder. With the measures of prediction adjustment, the near lossless compression option can be implemented on top of the lossless compression engine with minimized overhead. An efficient bit-rate control scheme is also developed and can support rate or distortion-constrained controls. For full HD (previously encoded) and nonfull HD test sequences, the lossless compression ratio of the proposed scheme, on average, is 21% and 46%, respectively, better than the Joint Photographic Experts Group-Lossless Standard and the Fast, Efficient Lossless Image Compression System (FELICS) schemes. The near lossless compression option can offer additional 6%–20% bit-rate reduction while keeping the Peak Signal-to-Noise Ratio value 50 dB or higher. The codec is further optimized complexity-wise to facilitate a high-throughput chip implementation. It features a five-stage pipelined architecture and two parallel computing kernels to enhance the throughput. Fabricated using the Taiwan semiconductor manufacturing company 90-nm complementary metal-oxide–semiconductor technology, the design can operate at 200 MHz and supports a 64 frames/s processing rate for full HD videos.

A PREDICTIVE CODING METHOD FOR LOSSLESS COMPRESSION OF IMAGES

Images are an important part of today's digital world. However, due to the large quantity of data needed to represent modern imagery the storage of such data can be expensive. Thus, work on efficient image storage (image compression) has the potential to reduce storage costs and enable new applications.This lossless image compression has uses in medical, scientific and professional video processing applications.Compression is a process, in which given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed.Compression is efficient for storing and transmission purpose.In this paper we described a new lossless adaptive prediction based algorithm for continuous tone images. In continuous tone images spatial redundancy exists.Our approach is to develop a new backward adaptive prediction techniques to reduce spatial redundancy in a image.The new prediction technique known as Modifed Gradient Adjusted Predictor (MGAP) is developed. MGAP is based on the prediction method used in Context based Adaptive Lossless Image Coding (CALIC). An adaptive selection method which selects the predictor in a slope bin in terms of minimum entropy improves the compression performance.

A near-lossless approach for medical image compression using visual quantisation and block-based DPCM

For the efficient transmission of medical image datasets over the internet, compression techniques which offer high compression ratio and less computational cost are required. Since the existing lossless techniques have low compression ratios, the alternative approach is to employ near-lossless methods. The existing near-lossless methods like JPEG-LS are context based and computationally intensive. In this paper, a new approach for near-lossless compression of the medical images is proposed. Pre-processing techniques are applied to the input image to generate a visually quantised image. The visually quantised image is encoded using a low complexity block-based lossless differential pulse code modulation coder, followed by the Huffman entropy encoder. The compression performance is compared with the state-of-the-art technique context-based adaptive lossless image coding, by using the objective analysis parameter peak signal to noise ratio and the image fidelity measurement parameter visual signal to noise ratio. Results show the superiority of the proposed technique in terms of the bit rate and visual quality.

Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction

Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.

Grayscale true two-dimensional dictionary-based image compression

Dictionary-based encoding methods are popular forms of data compression. These methods were initially implemented to reduce the one-dimensional correlation in data, since they are designed to compress text. Therefore, they do not take advantage of the fact that adjacent pixels in images are correlated in two dimensions. Previous attempts have been made to adapt dictionary-based compression schemes to consider the two-dimensional nature of images, but mostly for binary images. In this paper, a two-dimensional dictionary-based lossless image compression scheme for grayscale images is introduced. The proposed scheme reduces correlation in image data by finding two-dimensional blocks of pixels that are approximately matched throughout the data and replacing them with short codewords. Test results show that the compression performance of the proposed scheme outperforms and surpasses any other existing dictionary-based lossless compression scheme. The results also show that it slightly outperforms JPEG-2000s compression performance, when it operates in its lossless mode, and it is comparable to JPEG-LS’s and CALIC’s compression performance, where JPEG-2000 and JPEG-LS are the current image compression standards, and CALIC is a Context-based Adaptive Lossless Image Coding scheme.

Lossless image coding using adaptive, switching algorithm with automatic fuzzy context modelling

A switching adaptive predictor (SWAP) with automatic fuzzy context modelling is proposed for lossless image coding. Depending on the context of the coding pixel, the SWAP encoder switches between two predictors: the adaptive neural predictor (ANP) and the texture context matching (TCM) predictor. The ANP is known to perform well and gives small prediction errors except for pixels around edges. For areas with edges, TCM is used. To decide which is to be used, a switching criterion is proposed to pick out pixels around edges effectively. With the switching predictor structure, small prediction errors can be achieved in both slowly varying areas and edges. Furthermore, the use of the so-called fuzzy context clustering for prediction error refinement is proposed. The proposed compensation mechanism is proved to be very useful through experiments. It further improves the bit rates by, on average, 0.2 bpp in test images. The experiments also show that an average improvement of 0.3 and 0.05 bpp in first-order entropy can be achieved when the proposed switching predictor is compared with the gradient adjusted predictor and a six-order edge directed predictor, respectively. Moreover, the lossless image coder built upon the proposed algorithm also provides lower bit rates than the state-of-the-art context-based, adaptive, lossless image coding (CALIC) system and is comparable to that obtained by the highly complex two-pass coder called TMW.

Optimized Onboard Lossless and Near-Lossless Compression of Hyperspectral Data Using CALIC

We propose a new lossless and near-lossless compression algorithm for hyperspectral images based on context-based adaptive lossless image coding (CALIC). Specifically, we propose a novel multiband spectral predictor, along with optimized model parameters and optimization thresholds. The resulting algorithm is suitable for compression of data in band-interleaved-by-line format; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data shows that it outperforms 3-D-CALIC as well as other state-of-the-art compression algorithms.

Why does histogram packing improve lossless compression rates?

The performance of state-of-the-art lossless image coding methods [such as JPEG-LS, lossless JPEG-2000, and context-based adaptive lossless image coding (CALIC)] can be considerably improved by a recently introduced preprocessing technique that can be applied whenever the images have sparse histograms. Bitrate savings of up to 50% have been reported, but so far no theoretical explanation of the fact has been advanced. This letter addresses this issue and analyzes the effect of the technique in terms of the interplay between histogram packing and the image total variation, emphasizing the lossless JPEG-2000 case.