Comprehensive Review on Lossy and Lossless Compression Techniques

Elakkiya, S.; Thivya, K. S.

doi:10.1007/s40031-021-00686-3

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Immense amount of publications has indicated significant focus on the realm of multimedia compression, e.g. Kwan et al (2019), Elakkiya and Thivya (2021).…”

Section: Related Work Reviewmentioning

confidence: 99%

Combined Run-Length and Huffman Encoding for Image Compression

Krainyk

2022

Preprint

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The paper introduces combined Run-Length encoding (RLE) and Huffman encoding approach for image compression. The approach can be applied to the peculiar type of images that satisfies specific characteristics in terms of color components data. The devised approach is supposed to implement lossless compression for the preprocessed image data. First, the RLE is applied to the prepared data, then, Huffman coding process is performed. The results received during the test session of the implemented software showcase that the proposed technique is feasible and provides a decent level of compression. It can be implemented also in the hardware since the complexity of the operations used is relatively low.

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“…Immense amount of publications has indicated significant focus on the realm of multimedia compression, e.g. Kwan et al (2019), Elakkiya and Thivya (2021).…”

Section: Related Work Reviewmentioning

confidence: 99%

Combined Run-Length and Huffman Encoding for Image Compression

Krainyk

2022

Preprint

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“…Increasing resolution of video surveillance footage or increasing the length of retention would help resolve criminal cases and investigations. Data compression methodology includes lossless and lossy compression (2). Lossless compression results in reduced file sizes but allows for the original video footage to be regenerated without losing quality or resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Lossless compression results in reduced file sizes but allows for the original video footage to be regenerated without losing quality or resolution. Lossy compression allows much greater reduction in file sizes; however, the original high resolution video footage cannot be regenerated, therefore lossy compression results in loss of quality (2).…”

Section: Introductionmentioning

confidence: 99%

Deep residual neural networks for increasing the resolution of CCTV images

Bantval,

Akbari

2023

J Emerg Invest

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Images from closed-circuit television (CCTV) cameras are often stored with poor resolution due to technical and data storage limitations, reducing the utility of these images. In this study, we propose that informed design and training of an enhanced deep residual network (EDSR) can increase the resolution of CCTV images. This work is the first published application of the EDSR methodology for increasing CCTV image resolution. We utilized a dataset of 986 CCTV images that trained and tested an EDSR for generating high-resolution images from a low-resolution dataset. Implementing an EDSR model, we demonstrate that with 60 layers, performing image super-resolution is extremely effective, producing a peak signal-to-noise ratio value of 76.83 decibels. Furthermore, this technique may be used more generally as a technique for file compression enabling the storage of images at smaller file sizes at low-resolution and the ability to reproduce the same images in high-resolution form.

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Comparative Analysis of Intra- and Inter-Prediction Compression Techniques for Endoscopic Videos

Nandyal¹,

Gogi²

2022

Lecture Notes in Networks and Systems

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Comprehensive Review on Lossy and Lossless Compression Techniques

Cited by 9 publications

References 34 publications

Combined Run-Length and Huffman Encoding for Image Compression

Combined Run-Length and Huffman Encoding for Image Compression

Deep residual neural networks for increasing the resolution of CCTV images

Comparative Analysis of Intra- and Inter-Prediction Compression Techniques for Endoscopic Videos

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