Xiaolin Chen

Bio: Xiaolin Chen is an academic researcher from University of Bristol. The author has contributed to research in topics: Data compression & Lossless compression. The author has an hindex of 4, co-authored 6 publications receiving 46 citations.

Lossless Compression for Space Imagery in a Dynamically Reconfigurable Architecture

Abstract: This paper presents a novel dynamically reconfigurable hardware architecture for lossless compression and its optimization for space imagery. The proposed system makes use of reconfiguration to support optimal modeling strategies adaptively for data with different dimensions. The advantage of the proposed system is the efficient combination of different compression functions. For image data, we propose a new multi-mode image model which can detect the local features of the image and use different modes to encode regions with different features. Experimental results show that our system improves compression ratios of space image while maintaining low complexity and high throughput.

Backward Adaptive Pixel-based Fast Predictive Motion Estimation

Abstract: This letter presents a novel backward adaptive pixel-based fast predictive motion estimation (BAPME) scheme for lossless video compression. Unlike the widely used block-matching motion estimation techniques, this method predicts the motion on a pixel-by-pixel basis by comparing a group of past observed pixels in 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 suggest that BAPME is superior to block-based full search in terms of speed and zero-order entropy.

Hardware architecture for lossless image compression based on context-based modeling and arithmetic coding

Abstract: In this paper we present a novel hardware architecture for context-based statistical lossless image compression, as part of a dynamically reconfigurable architecture for universal lossless compression. A gradient-adjusted prediction and context modeling algorithm is adapted to a pipelined scheme for low complexity and high throughput. Our proposed system improves image compression ratio while keeping low hardware complexity. This system is designed for a Xilinx Virtex4 FPGA core and optimized to achieve a 123 MHz clock frequency for real-time processing.

Lossless Multi-Mode Interband Image Compression and Its Hardware Architecture

Abstract: This paper presents a novel Lossless Multi-Mode Interband image Compression (LMMIC) scheme and its hardware architecture. Our approach detects the local features of the image and uses different modes to encode regions with different features adaptively. Run-mode is used in homogeneous regions, while ternary-mode and regular-mode are used on edges and other regions, respectively. In regular mode, we propose a simple band shifting technique as interband prediction and a gradient-based switching strategy to select between intraband or interband prediction. We also enable intraband and interband adaptation in the run-mode and ternary-mode. The advantage of LMMIC is to adaptively “segment” the image and use suitable methods to encode different regions. The simplicity of our scheme enables the hardware amenability. Experimental results show that LMMIC achieves superior compression ratios, with the benefits of enabling encoding any number of bands and easy access to any band. We also describe the hardware architecture for this scheme.

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

Abstract: 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.

A Lossless Color Image Compression Architecture Using a Parallel Golomb-Rice Hardware CODEC

Abstract: In this paper, a high performance lossless color image compression and decompression architecture to reduce both memory requirement and bandwidth is proposed. The proposed architecture consists of differential-differential pulse coded modulation (DDPCM) and Golomb-Rice coding. The original image frame is organized as m by n sub-window arrays, to which DDPCM is applied to produce one seed and m × n - 1 pieces of differential data. Then the differential data are encoded using the Golomb-Rice algorithm to produce losslessly compressed data. According to the experimental results on benchmark images, the proposed architecture can guarantee high enough compression rate and throughput to perform real-time lossless CODEC operations with a reasonable hardware area.

Efficient SKIP Mode Detection for Coarse Grain Quality Scalable Video Coding

Abstract: Scalable video coding (SVC) was recently standardized by the Joint Video Team as an extension of H.264. In SVC, a computationally expensive exhaustive mode decision is employed to select the best coding mode for each macroblock (MB), which achieves a high coding efficiency. In order to reduce computational complexity, we propose an efficient SKIP mode detection approach for coarse grain quality SVC. It makes use of the coding information of spatial neighboring MBs and the co-located MB in base layer to predict the SKIP mode MB and early terminate its mode decision procedure. Experimental results show that the proposed early SKIP mode decision approach can achieve the average computational saving about 54% with almost no loss of rate distortion (RD) performance in the enhancement layer.

Fast and adaptive lossless on-board hyperspectral data compression system for space applications

Abstract: Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed ‘Fast Lossless’ algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware, which makes it practical for flight implementations of pushbroom instruments. A prototype of the compressor (and decompressor) of the algorithm is available in software, but this implementation may not meet speed and real-time requirements of some space applications. Hardware acceleration provides performance improvements of 10x–100x vs. the software implementation (about 1M samples/sec on a Pentium IV machine). This paper describes a hardware implementation of the ‘Fast Lossless’ compression algorithm on a Field Programmable Gate Array (FPGA). The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for Space applications.

Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space

Abstract: Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed ‘Fast Lossless’ algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. It was modified for pushbroom instruments and makes it practical for flight implementations. A prototype of the compressor (and decompressor) of the algorithm is available in software, but this implementation may not meet speed and real-time requirements of some space applications. Hardware acceleration provides performance improvements of 10x-100x vs. the software implementation (about 1M samples/sec on a Pentium IV machine). This paper describes a hardware implementation of the ‘Modified Fast Lossless’ compression algorithm for pushbroom instruments on a Field Programmable Gate Array (FPGA). The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for Space applications.