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

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 Comparative Analysis of Image Compression using PCM and DPCM

The quality of the signal is essential in digital communication and signal processing. The transmission channel is also important. Modulation is used for effectively transmission of signal. There exist several types of modulation techniques. One of such is the pulse code modulation (PCM). The performance of PCM is however affected by quantization error and noise in the transmission channel, which affects the quality of the output. Against this backdrop, this paper presents the use of differential pulse code modulation (DPCM) so as to address the limitation of pulse code modulation. The simulation environment is MATLAB 2018a. The MATLAB Simulink is used to design the PCM and DPCM systems using appropriate digital processing blocks. The DPCM system shows a significant improvement in terms of error reduction and quality of output.

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

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.

Processing of Digital Signals in (+1, -1) Binary System

Digital Signal Processing in (+1, -1) system has been studied in detail. Effects of number representation on quantization and truncation of rounding have been discussed. First order and second order filters have been investigated. Differential pulse code modulation method of coding is being studied. It is found that this system is well suited for VLSI design. The hardware realization of different components of DPCM is easy and straight. The position and negative signals can be processed in a unified way. It gives better performance in comparison of two’s complement representation of conventional binary system. The effect of limit cycles, stability and round off noise in first and second order filter have been discussed too.

Аdaptive combined image coding with prediction of arithmetic code volume

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.