Lossless Image Compression Techniques: A State-of-the-Art Survey

Rahman, Md. Atiqur; Hamada, Mohamed

doi:10.3390/sym11101274

Cited by 62 publications

(33 citation statements)

References 32 publications

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“…In this paper, we provide a detailed analysis of the state-of-the-art lossless still image compression techniques. As we have seen, most previous survey papers on lossless image compression focus on the CR [3,4,16,78,[95][96][97][98]. Although this is an important measurement criterion, the ET and DT are two further important factors for lossless image compression.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…One solution from the software perspective is compression. Data compression is a way of representing data using fewer bits than the original in order to reduce the consumption of storage and bandwidth and increase transmission speed over networks [3]. Data compression can be applied in many areas, such as audio, video, text, and images, and it can be classified into two categories: lossless and lossy [4].…”

Section: Introductionmentioning

confidence: 99%

“…Although there are also many types of image, such as binary images, 8-bit and 16-bit grayscale images, 8-bit indexed images, and 8-bit and 16-bit red, green, and blue (RGB) images, we only cover 8-bit and 16-bit grayscale and RGB images in this article. A detailed review of run-length, Shannon-Fano, LZW, Huffman, and arithmetic coding was carried out in [3]. Four parameters are used to evaluate a lossless image compression algorithm: the compression ratio (CR), bits per pixel (bpp), encoding time (ET), and decoding time (DT), and bpp is the simply the inverse of the CR; therefore, we only consider the CR, ET, and DT when evaluating these methods.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

2021

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A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

2021

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“…The lossy compression algorithm has been dominant owing to the relatively high compression ratio [ 8 ]. However, in the case of image compression for constructing a GUI, a lossless compression can achieve a high compression ratio, as similar as to the lossy compression, while maintaining image quality [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Lossless Decompression Accelerator for Embedded Processor with GUI

Hwang

Cho

et al. 2021

Micromachines

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The development of the mobile industry brings about the demand for high-performance embedded systems in order to meet the requirement of user-centered application. Because of the limitation of memory resource, employing compressed data is efficient for an embedded system. However, the workload for data decompression causes an extreme bottleneck to the embedded processor. One of the ways to alleviate the bottleneck is to integrate a hardware accelerator along with the processor, constructing a system-on-chip (SoC) for the embedded system. In this paper, we propose a lossless decompression accelerator for an embedded processor, which supports LZ77 decompression and static Huffman decoding for an inflate algorithm. The accelerator is implemented on a field programmable gate array (FPGA) to verify the functional suitability and fabricated in a Samsung 65 nm complementary metal oxide semiconductor (CMOS) process. The performance of the accelerator is evaluated by the Canterbury corpus benchmark and achieved throughput up to 20.7 MB/s at 50 MHz system clock frequency.

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“…A way of representing data using fewer bits than an original is called compression. Compression reduces the consumption of storage and bandwidth; and increases transmission speed over the cyber world [3][4]. Compression is applied in many areas such as audio, video, text, images, Etc.…”

Section: Introductionmentioning

confidence: 99%

Lossless text compression using GPT-2 language model and Huffman coding

Rahman

Hamada

2021

SHS Web Conf.

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Modern daily life activities produced lots of information for the advancement of telecommunication. It is a challenging issue to store them on a digital device or transmit it over the Internet, leading to the necessity for data compression. Thus, research on data compression to solve the issue has become a topic of great interest to researchers. Moreover, the size of compressed data is generally smaller than its original. As a result, data compression saves storage and increases transmission speed. In this article, we propose a text compression technique using GPT-2 language model and Huffman coding. In this proposed method, Burrows-Wheeler transform and a list of keys are used to reduce the original text file’s length. Finally, we apply GPT-2 language mode and then Huffman coding for encoding. This proposed method is compared with the state-of-the-art techniques used for text compression. Finally, we show that the proposed method demonstrates a gain in compression ratio compared to the other state-of-the-art methods.

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Lossless Image Compression Techniques: A State-of-the-Art Survey

Cited by 62 publications

References 32 publications

The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

Lossless Decompression Accelerator for Embedded Processor with GUI

Lossless text compression using GPT-2 language model and Huffman coding

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