Realizing Transparent OS/Apps Compression in Mobile Devices at Zero Latency Overhead

Zhang, Xuebin; Li, Jiangpeng; Wang, Hao; Xiong, Danni; Qu, Jerry; Shin, Hyun-Suk; Zhang, Tong

doi:10.1109/tc.2017.2664838

Cited by 13 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We implemented the architecture on Xilinx xcku-095 and measured the CR, throughput, and hardware resource usage. We used chunk-based compression that is widely adopted in systems using compression [28], [29]. Chunk-based compression allows hardware accelerators to be implemented with less memory than file-based compression [28].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Design of FPGA-Based LZ77 Compressor With Runtime Configurable Compression Ratio and Throughput

et al. 2019

View full text Add to dashboard Cite

Data compression reduces the cost of data storage and transmission by decreasing the data size. Previous studies have improved system performance by adaptively choosing the compression ratio (CR) and throughput required for the system by using a trade-off between them in the compression algorithm. Hardware accelerators are widely used to reduce the CPU load caused by compression operations. Several existing compression accelerators have low flexibility in changing the CR and bandwidth. This study proposes a hardware compression accelerator that can adjust the CR and throughput at runtime. The proposed architecture accelerates the LZ77 compression algorithm and supports the throughput-first (TF) and compression ratio-first (CF) modes by changing the degree of parallelism of comparison operations performed during the compression process. In addition, we propose a technique to dynamically change the degree of parallelism of the comparison operation to achieve a better throughput in CF mode and a better CR in TF mode. Experimental results demonstrate that the TF mode provides a throughput higher by 11.39%, and a CR lower by 0.07 than the CF mode. The value 0.07 accounts for 13.21% of the variation in the CR provided by the software implementation of LZ77.INDEX TERMS Accelerator architectures, data compression, field programmable gate arrays.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Design of FPGA-Based LZ77 Compressor With Runtime Configurable Compression Ratio and Throughput

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The general DEFLATE algorithm performs Huffman encoding after the LZ77 encoder processes enough input data streams so that an intermediate data block of a certain size is created. However, chunk-based compression [48], [49] divides the data stream into a number of fixed-size chunks, which are compressed independently. In this case, the input data size of the Huffman encoder changes according to the compression efficiency of the LZ77 encoding.…”

Section: ) Enhanced Heuristic-based Compressibility Predictionmentioning

confidence: 99%

“…First, previous studies do not fully account for changes in the system in which compression works. In storage systems, compression is typically performed on fixed-size chunks of data, for a variety of reasons, such as read amplification, metadata overhead, and IO parallelism [48], [49]. In this case, the compression efficiency of the preceding compression algorithm can affect the compression efficiency of subsequent compression algorithms.…”

Section: Introductionmentioning

confidence: 99%

Low-Overhead Compressibility Prediction for High-Performance Lossless Data Compression

et al. 2020

View full text Add to dashboard Cite

As big data has evolved over the past few years, a lack of storage space and I/O bandwidth has become one of the most important challenges to overcome. To mitigate these problems, data compression schemes reduce the amount of data to be stored and transmitted at the cost of additional CPU overhead. Many researchers have attempted to reduce the computational load imposed on the CPU by data compression using specialized hardware. However, space savings through data compression often comes from only a small portion of data. Therefore, compressing all data, regardless of data compressibility, can waste computational resources. Our work aims to decrease the cost of data compression by introducing a selective data compression scheme based on data compressibility prediction. The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression. Additionally, our method reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost. To verify the proposed scheme, we implemented a DEFLATE compression system on a field-programmable gate array platform. Experimental results demonstrate that the proposed scheme improves compression throughput by 34.15% with a negligible decrease in compression ratio. INDEX TERMS Data compression, Huffman coding, LZ77 encoding, accelerator architecture, field programmable gate array, estimation, compressibility.

show abstract

“…Considering the incomparable advantages of image information over other types of information, it is important to process images appropriately in the different fields [1]. In image acquisition, processing, transmitting, and recording, image distortion and quality degradation are an inevitable result of the imperfection of the imaging system, the processing method, the transmission medium, and the recording equipment, as well as object movement and noise pollution [2,3,4]. Image quality has a direct effect on people's subjective feelings and information acquisition.…”

Section: Introductionmentioning

confidence: 99%

No-reference color image quality assessment: from entropy to perceptual quality

Chen

Zhang

Lin

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The source code is available on github https://github.com/jacob6/ENIQA.

show abstract

Realizing Transparent OS/Apps Compression in Mobile Devices at Zero Latency Overhead

Cited by 13 publications

References 12 publications

Design of FPGA-Based LZ77 Compressor With Runtime Configurable Compression Ratio and Throughput

Design of FPGA-Based LZ77 Compressor With Runtime Configurable Compression Ratio and Throughput

Low-Overhead Compressibility Prediction for High-Performance Lossless Data Compression

No-reference color image quality assessment: from entropy to perceptual quality

Contact Info

Product

Resources

About