Golomb-Rice Coding Research Articles

Fixed-Ratio Compression of an RGBW Image and Its Hardware Implementation

In the latest large-screen TVs, an RGBW color domain is widely used to enhance the brightness of liquid crystal display (LCD) panels. As the correlations among RGBW color components are different from those among conventional RGB components, a compression algorithm developed for an RGB image may not be efficient for RGBW compression. For high-fidelity RGBW image coding, a novel prediction-based coding algorithm is proposed in this paper. The proposed algorithm is composed of two prediction steps that take advantage of spatial correlation and inter-color correlations, respectively. For residual coding, this paper proposes a fixed-ratio coding method based on Golomb-Rice coding. The proposed encoder is composed of two sub-coders: pre-coder and post-coder. The pre-coder estimates the length of the bitstream for various quantization levels. Based on that estimation, the appropriate quantization level is selected. The post-coder generates the final bitstream. Experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed algorithm is 51.16 dB. The throughput of the proposed encoder and decoder hardwares is 26.7 Gb/s, and their respective gate counts are 56.1 K and 39.6 K.

Improving JPEG-LS Performance Using Location Information

JPEG-LS is an international standard for lossless or near-lossless image-compression algorithms. In this paper, a simple method is proposed to improve the performance of the lossless JPEG-LS algorithm. With respect to JPEG-LS and its supplementary explanation, Golomb-Rice (GR) coding is mainly used for entropy coding, but it is not used for long codewords. The proposed method replaces a set of long codewords with a set of shorter location map information. This paper shows how efficiently the location map guarantees reversibility and enhances the compression rate in terms of performance. Experiments have also been conducted to verify the efficiency of the proposed method.

Onboard Image Processing System for Hyperspectral Sensor.

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

Preprocessing and Golomb-Rice Encoding for Lossless Compression of Phasor Angle Data

Phasor measurement units (PMUs) are being increasingly deployed to improve monitoring and control of the power grid due to their improved data synchronization and reporting rates in comparison with legacy metering devices. However, one drawback of their higher data rates is the associated increase in bandwidth (for transmission) and storage requirements (for data archives). Fortunately, typical grid behavior can lead to significant compression opportunities for phasor angle measurements. For example, operation of the grid at near-nominal frequency results in small changes in phase angles between frames, and the similarity in frequencies throughout the system results in a high level of correlation between phasor angles of different PMUs. This paper presents several methods for preprocessing of phasor angles that take advantage of these system characteristics, including a new method—frequency compensated difference encoding—that is able to significantly reduce angle data entropy. After the preprocessor stage, the signal is input to an entropy encoder, based on Golomb–Rice codes, that is ideal for high-throughput signal compression. The ability of the proposed methods to compress phase angles is demonstrated using a large corpus of data—over 1 billion phasor angles from 25 data sets—captured during typical and atypical grid conditions.

Robust and fast selective encryption for HEVC videos

Emerging High efficiency video coding (HEVC) is expected to be widely adopted in network applications for high definition devices and mobile terminals. Thus, construction of HEVC's encryption schemes that maintain format compliance and bit rate of encrypted bitstream becomes an active security's researches area. This paper presents a novel selective encryption technique for HEVC videos, based on enciphering the bins of selected Golomb–Rice code’s suffixes with the Advanced Encryption Standard (AES) in a CBC operating mode. The scheme preserves format compliance and size of the encrypted HEVC bitstream, and provides high visual degradation with optimized encryption space defined by selected Golomb–Rice suffixes. Experimental results show reliability and robustness of the proposed technique.

Design of a smart-device and FPGA based wireless capsule endoscopic system

The paper presents a modular and programmable design of a next generation wireless capsule endoscopic system (WCES). The system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip that interfaces directly with Android based smart-devices (smartphone or tablet) and consumes 12.42mW of power. As a result, image data from an endoscopic capsule can be saved (or viewed) directly on smart-device, tablet, laptop, workstation or secured server connected to a hospital's or residential Wi-Fi network. This eliminates the need to carry a bulky data logger and enables medical doctors to analyze data and perform diagnosis in real time from a remote place. The system supports full-duplex data communication between the endoscopic capsule and smart-device which further allows doctors to control many features of the capsule in real-time. Due to the flexible and reprogrammable nature of the system, various image processing and compression algorithms can be employed without any change in hardware. The system supports multi-spectral imaging including white light imaging (WLI) and narrow band imaging (NBI). A variable-length, low-complexity image compression algorithm is used that is based on a novel color-space followed by an optimized Golomb-rice coding. The entire system is prototyped using circular PCBs with a size of 16mm×36mm. An Android mobile app is designed with many features to control the operation of the capsule as well as display the captured images. Both in vivo and ex vivo trials using live pig have been conducted to validate the advantage of the proposed system.

Design of forward adaptive hybrid quantiser with Golomb–Rice code for compression of Gaussian source

This study proposes a novel model of hybrid quantiser composed of a uniform scalar quantiser and a non-uniform optimal companding scalar quantiser, both designed for a Gaussian source. We examine whether by appropriately designing a novel forward adaptive hybrid quantiser with Golomb–Rice code, one can achieve more sophisticated compression and a higher signal to quantisation noise ratio compared with the uniform quantiser with Golomb–Rice code. We observe which value of the bit rate should be chosen to provide high-quality quantisation. It is shown that the authors compression model can satisfy G.712 recommendation for high-quality quantisation achieving the compression of 1.68 bit/sample over the G.711 quantiser. In addition, for the average bit rate of 6.32 bit/sample their hybrid quantiser outperforms the uniform quantiser for 1.32 dB. The presented performances of the forward adaptive hybrid quantiser indicate that it should be of theoretical and practical significance in quantisation of the Gaussian source signals.

An Optimum Novel Technique Based on Golomb-Rice Coding for Lossless Image Compression of Digital Images

An Optimum Novel Technique Based on Golomb-Rice Coding for Lossless Image Compression of Digital Images

Design of the adaptive piecewise uniform scalar quantizer with lossless coder and Golomb-Rice code on the output, for signals with Gaussian distribution

In this paper a new compression model for signals with Gaussian distribution is presented. It consists of a piecewise uniform scalar quantizer, a lossless coder and Golomb-Rice code. Forward adaptation of this proposed model is done and constant signal-to-quantization noise ratio (SQNR) in wide range of input variances is obtained. Joined design of lossy and lossless coder is done, achieving optimal performances. Our model has small complexity since all components of the model (piecewise uniform quantizer, lossless coder and Golomb-Rice code) are very simple for realization. As an example, this model is applied on the speech signal, and it is shown that the G.712 standard is satisfied with bit-rate decrease of 1.09 bits/sample, compared to the classic uniform quantizer.

An Optimum Novel Technique Based on Golomb-Rice Coding for Lossless Image Compression of Digital Images

New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm which efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of Peak Signal to Noise Ratio (PSNR) in two directions of research. We considered medical images , satellite extracted images and natural images for the inspection and proposed a novel procedure named as Novel Optimized Golomb Rice Coding (NOGR) to increase the visual quality of the reconstructed image. The result of the proposed technique outperforms present techniques and the results are simulated using MATLAB.

Improved entropy coding for quantized transform coefficients in HEVC screen content coding

In the emerging high efficiency video coding (HEVC) standard, a Golomb-Rice code is adopted to binarize level information. The Golomb-Rice code is optimal for encoding symbols with the exponential probability distribution. In general, quantized transform coefficients of natural images show the exponential probability distribution. However, screen contents usually have a totally different probability distribution, and the Golomb-Rice code is not appropriate for screen content coding. In this paper, we focus on a new entropy coding scheme for screen content coding. In the proposed scheme, we clip inefficient high magnitude coefficient levels that do not fit the exponential probability distribution. Then, we apply a limited length Golomb-Rice code to binarize clipped levels. From experimental results, it is verified that the proposed method achieves on average 0.60 % BD-rate saving and up to 1.13 % BD-rate saving, compared to HEVC screen content coding. When the proposed method is combined with a well-known screen content coding technique, transform skipping, it shows up to 24.02 % BD-rate saving.

Design of VLSI Architecture of Autocorrelation-Based Lossless Recompression Engine for Memory-Efficient Video Coding Systems

In this paper, an autocorrelation-based lossless recompression (ABLR) algorithm is proposed. The ABLR can save the memory bandwidth of video coding systems and preserves the visual quality. The ABLR consists of two core techniques: (1) a correlation-based prediction technique and (2) a correlation-adaptive Golomb-Rice code. Furthermore, dual-mode memory addressing (DMMA) is also proposed to provide ABLR with memory random access functionality. The word-length utilization rate (WLUR) of DMMA is as high as 92.34 % on average. The experimental results reveal that the ABLR exhibits a lossless compression ratio of 2.05 on average for 1080p test sequences. This indicates that the memory bandwidth can be saved up to 50 %. The VLSI architecture of ABLR is designed with three-stage pipelining and is realized in 0.18 μm 1P6M CMOS technology with a cell-based design flow. The logic gate count is about 28 K and the core area is 0.69×0.68 mm2. The encoding capability can reach full HD (1920×1080)@30 fps at a clock rate of 62.5 MHz. The power dissipation is 9.35 mW at a clock rate of 62.5 MHz.

Low-complex energy-aware image communication in visual sensor networks

A low-complex, low bit rate, energy-efficient image compression algorithm explicitly designed for resource-constrained visual sensor networks applied for surveillance, battle field, habitat monitoring, etc. is presented, where voluminous amount of image data has to be communicated over a bandwidth-limited wireless medium. The proposed method overcomes the energy limitation of individual nodes and is investigated in terms of image quality, entropy, processing time, overall energy consumption, and system lifetime. This algorithm is highly energy efficient and extremely fast since it applies energy-aware zonal binary discrete cosine transform (DCT) that computes only the few required significant coefficients and codes them using enhanced complementary Golomb Rice code without using any floating point operations. Experiments are performed using the Atmel Atmega128 and MSP430 processors to measure the resultant energy savings. Simulation results show that the proposed energy-aware fast zonal transform consumes only 0.3% of energy needed by conventional DCT. This algorithm consumes only 6% of energy needed by Independent JPEG Group (fast) version, and it suits for embedded systems requiring low power consumption. The proposed scheme is unique since it significantly enhances the lifetime of the camera sensor node and the network without any need for distributed processing as was traditionally required in existing algorithms.

A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images

The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on Golomb-Rice coding which was efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of peak signal-to-noise ratios (PSNR). We considered medical images, satellite extracted images, and natural images for the inspection and proposed a novel technique to increase the visual quality of the reconstructed image.

Efficient fuzzy‐controlled and hybrid entropy coding strategy lossless ECG encoder VLSI design for wireless body sensor networks

An efficient VLSI design of a lossless electrocardiogram (ECG) encoder is proposed for wireless body sensor networks. To save wireless transmission power, a novel lossless encoding algorithm had been created for ECG signal compression. The proposed algorithm consists of a novel adaptive predictor based on fuzzy decision control, and a novel hybrid entropy encoder including both a two-stage Huffman and a Golomb-Rice coding. The VLSI architecture contains only 2.71 K gate counts and its core area is 33 929 μm 2 synthesized by a 0.18 μm CMOS process. Moreover, this design can be operated at 100 MHz processing rate by consuming only 30 μW. It achieves an average compression rate of 2.56 for the MIT-BIH arrhythmia database. Compared with previous low-complexity and high-performance lossless ECG encoder studies, this design has a higher compression rate, lower power consumption and lower hardware cost than other VLSI designs.

GPU-Based High Performance Terrain Compression

It has been well accepted that compression is essential to the real-time visualization of large-terrain elevation data. To achieve efficient performance, a terrain compression method featuring high decoding speed is proposed in this paper. A combined prediction scheme is applied in the prediction stage. Golomb-Rice coding is then used to encode the residuals after the prediction stage. The style of the codeword is tailored to make the decoding process amenable to a GPU implementation. Then batching is used to further improve the decoding speed. The proposed method is easy to implement and simple to integrate into existing systems. Experiments show that the compression rate is better than PNG. In terms of decompression efficiency, this method archives a decoding speed of over 5.8 Gpix/s, which is much faster than most existing systems.

Adaptive Golomb Code for Joint Geometrically Distributed Data and Its Application in Image Coding

This paper proposes joint-probability-based adaptive Golomb coding (JPBAGC) to improve the performances of the Golomb family of codes, including Golomb coding (GC), Golomb–Rice coding (GRC), exp-Golomb coding (EGC), and hybrid Golomb coding (HGC), for image compression. The Golomb family of codes is ideally suited to the processing of data with geometric distribution. Since it does not require a coding table, it has higher coding efficiency than Huffman coding. In this paper, we find that there are many situations in which the probability distribution of data is not only geometric, but also depends on the probability distribution of the other data. Accordingly, we used the joint probability of generalizing the Golomb family of codes and exploiting the dependence between neighboring image data. The proposed JPBAGC improves the efficiency of many image and video compression standards, such as the joint photographic experts group (JPEG) compression scheme and the H.264-intra JPEG-based image coding system. Simulation results demonstrate the superior coding efficiency of the proposed scheme over those of Huffman coding, GC, GRC, EGC, and HGC.

Modified Golomb-Rice Algorithm for Color Image Compression

The memory required to store the color image is more. We have reduced the memory requirements using Golomb-rice algorithm. Golomb-rice algorithm consists of the following two steps. In Golomb-Rice algorithm the first step is to compress the image using discrete wavelet transform. By using DWT compression the 8 × 8 image is converted into m × n sub-windows and it is converted into raster file format for producing m × n-1 differential data. Encoding is done by using Golomb-Rice coding. After encoding, the process length, code word and size are calculated by using GR coding.In the second step decoding is done by GR coding based on the obtained length and code word. After that decoded image is decompressed in order to get the original image by using the inverse discrete wavelet transform.

Designing of the forward adaptive companding quantizer with variable length codewords for stohastic measurement signals

This paper deals with the designing of the forward adaptive ?-law companding quantizer whose levels are coded with the Golomb-Rice code. The designing is performed for measurement signals with the Gaussian distribution and applied for the speech signal. The model satisfies the G. 712 standard and achieves the decreasing of the bit-rate for 1.34 bps (bits per sample) compared to the G. 711 standard.

Lossless Compression of Colour Mosaic Video Frames Suitable for Progressive Transmission

Video transmission is a vital area in the era of communication. Video are of huge in size comparing to textual data, video transmission takes larger bandwidth of the communication channel. Since channel capacity is limited, the video transmission takes longer time. Thus video compression becomes an essential solution. Considering these factors this paper propose a way by which video is converted as a series of mosaic images and compressed for transmission. The entire process can be given in nutshell with few steps. Initially the RGB color video frames are converted into mosaic images according to Bayer Pattern. The mosaic image is one third of the size of RGB color image. Then the mosaic image is transformed to reduce the bits required to store the data. Two transformations are applied to this mosaic image. Firstly the difference between the color components of same color is computed. Since the same color components are interleaved slightly modified DPCM is used in this transformation. Secondly the inter-frame difference is computed. Finally the transformed mosaic image is compressed using Golomb Rice Code which is suitable for progressive transmission.