scholarly journals Lossless Compression Algorithm and Architecture for Reduced Memory Bandwidth Requirement with Improved Prediction Based on the Multiple DPCM Golomb-Rice Algorithm

2021 ◽  
Author(s):  
Imjae Hwang ◽  
Juwon Yun ◽  
Woonam Chung ◽  
Jaeshin Lee ◽  
Cheong-Ghil Kim ◽  
...  
Keyword(s):  
High Efficiency ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Compression Algorithm ◽  
Memory Bandwidth ◽  
High Efficiency Video Coding ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Bandwidth Requirement ◽  
Code Modulation

In a computing environment, higher resolutions generally require more memory bandwidth, which inevitably leads to the consumption more power. This may become critical for the overall performance of mobile devices and graphic processor units with increased amounts of memory access and memory bandwidth. This paper proposes a lossless compression algorithm with a multiple differential pulse-code modulation variable sign code Golomb-Rice to reduce the memory bandwidth requirement. The efficiency of the proposed multiple differential pulse-code modulation is enhanced by selecting the optimal differential pulse code modulation mode. The experimental results show compression ratio of 1.99 for high-efficiency video coding image sequences and that the proposed lossless compression hardware can reduce the bus bandwidth requirement.

A Fuzzy Switched DPCM Predictor for Lossless Compression of Noisy Images

1997 ◽  
Vol 05 (06) ◽  
pp. 689-699 ◽  
Author(s):  
H. B. Mitchell ◽  
D. D. Estrakh
Keyword(s):  
Substantial Reduction ◽  
Fuzzy Rule ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Critical Element ◽  
Pulse Code Modulation ◽  
Compression Efficiency ◽  
Rule Based ◽  
Differential Pulse Code Modulation ◽  
Code Modulation

Differential pulse code modulation (DPCM) algorithms are widely used to compress gray-scale pictures. A critical element in a DPCM algorithm is the accurate prediction of the pixel gray-levels in the input picture. The switched predictors used in modern DPCM algorithms are generally accurate when the input picture is free of noise. However, even small amounts of noise in the input picture will cause a substantial reduction in prediction accuracy and in turn a reduction in compression efficiency. In this paper we describe a novel fuzzy rule-based predictor for use in a lossless DPCM algorithm. For noisy pictures, the accuracy of the new predictor is consistently higher than the accuracy of standard switched predictors.

scholarly journals Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4602
Author(s):  
Shinichi Yamagiwa ◽  
Yuma Ichinomiya
Keyword(s):  
Data Compression ◽  
High Speed ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Video Stream ◽  
Video Encoding ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Code Modulation ◽  
Lossless Data Compression

Video applications have become one of the major services in the engineering field, which are implemented by server–client systems connected via the Internet, broadcasting services for mobile devices such as smartphones and surveillance cameras for security. Recently, the majority of video encoding mechanisms to reduce the data rate are mainly lossy compression methods such as the MPEG format. However, when we consider special needs for high-speed communication such as display applications and object detection ones with high accuracy from the video stream, we need to address the encoding mechanism without any loss of pixel information, called visually lossless compression. This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. However, the conventional ADPCM does not have any mechanism to control dynamically the encoding bit length. We propose a novel ADPCM that provides a mechanism with a variable bit-length control, called ADPCM-VBL, for the encoding/decoding mechanism. Furthermore, since we expect that the encoded data from ADPCM maintains low entropy, we expect to reduce the amount of data by applying a lossless data compression. Applying ADPCM-VBL and a lossless data compression, this paper proposes a video transfer system that controls throughput autonomously in the communication data path. Through evaluations focusing on the aspects of the encoding performance and the image quality, we confirm that the proposed mechanisms effectively work on the applications that needs visually lossless compression by encoding video stream in low latency.

Entropy-coded hybrid differential pulse-code modulation

1983 ◽  
Vol 19 (2) ◽  
pp. 63 ◽  
Author(s):  
N.M. Nasrabadi ◽  
S.K. Pal ◽  
R.A. King
Keyword(s):  
Differential Pulse ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Code Modulation

scholarly journals GPU-Based Lossless Compression of Aurora Spectral Data using Online DPCM

2019 ◽  
Vol 11 (14) ◽  
pp. 1635 ◽  
Author(s):  
Jiaojiao Li ◽  
Jiaji Wu ◽  
Gwanggil Jeon
Keyword(s):  
Spectral Data ◽  
Matrix Multiplication ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Acquisition Time ◽  
Time Interval ◽  
Processing Unit ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Redundant Data

It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indispensable. This paper presents a parallel Compute Unified Device Architecture (CUDA) implementation of the prediction-based online Differential Pulse Code Modulation (DPCM) method for the lossless compression of the aurora spectral data. Two improvements are proposed to improve the compression performance of the online DPCM method. One is on the computing of the prediction coefficients, and the other is on the encoding of the residual. In the CUDA implementation, we proposed a decomposition method for the matrix multiplication to avoid redundant data accesses and calculations. In addition, the CUDA implementation is optimized with a multi-stream technique and multi-graphics processing unit (GPU) technique, respectively. Finally, the average compression time of an aurora spectral image reaches about 0.06 s, which is much less than the 15 s aurora spectral data acquisition time interval and can save a lot of time for transmission and other subsequent tasks.

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