Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients

Ulacha, G.; Łazoryszczak, M.

doi:10.3390/e25010156

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…However, previous studies demonstrate higher compression ratios for more advanced prediction-based encoders for lossless compression, such as JPEG-LS [24,25], FLIF, and JPEG XL lossless [26]. Prediction methods are more commonly used for reducing information entropy for lossless image compression [27]. However, these methods are domain-dependent, whereas the proposed transformation methods are general.…”

Section: Methodsmentioning

confidence: 99%

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A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

Žalik,

Strnad,

Podgorelec

et al. 2023

Entropy

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This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.

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Section: Methodsmentioning

confidence: 99%

“…At this point, it is worth mentioning that string transformation techniques are less efficient than the modern prediction methods in the domain of 2D continuous-tone raster images [27,35]. In future work, it would be interesting to investigate the combination of prediction-based methods and the proposed MwI transformation.…”

mentioning

confidence: 99%

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

Žalik,

Strnad,

Podgorelec

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Some scholars have proposed a new compression method, which can predict based on linear and non-linear methods. The prediction error coding uses two-level compression, adopting adaptive Golomb coding and binary arithmetic coding [13]. Some scholars have simplified the hardware occupancy of JPEG-LS by specific optimization in the hardware implementation process [14].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Image Row-Compression Method for High-Definition USB Cameras Based on FPGA

Xie,

Zhang,

Zhao

2024

IEEE Access

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A real-time image compression method based on field programmable gate array is proposed for the problem of high-frame-rate high-resolution camera image transmission under the limited bandwidth of universal serial bus (USB). This method quantizes image pixels on a per-row basis, taking advantage of the high correlation between adjacent pixels within a row, thus reducing the data volume of a single frame image transmission. The algorithm also aims to minimize the decoding complexity on the central processor of the receiving end. Corresponding hardware circuits and software programs are designed, and tests are conducted on an experimental platform. The experimental results show that this method effectively compresses image data losslessly on the board, improves the transmission frame rate. The maximum frame rate of 1280x1280 images tested in a USB 2.0 environment can reach 25.58 fps, an improvement of 11.67 fps compared to the original data transfer, with a compression rate of up to 55.8%. Furthermore, this method outperforms PNG decoding in terms of decoding speed, supports multi-core decoding, and achieves the highest decoding speed of 61fps when tested on the 1920x1080 image with 16 threads. This method provides a feasible transfer solution for real-time compressed transmission of high-speed and highdefinition cameras in the industrial field.

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“…There are, however, other prediction models. An overview of them can be found in a very recent paper by Ulacha and Łazoryszczak [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

FLoCIC: A Few Lines of Code for Raster Image Compression

Žalik

Strnad

Kohek

et al. 2023

Entropy

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A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

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Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients

Cited by 5 publications

References 30 publications

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

A Real-Time Image Row-Compression Method for High-Definition USB Cameras Based on FPGA

FLoCIC: A Few Lines of Code for Raster Image Compression

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