JPEG Quantization Table Optimization by Guided Fireworks Algorithm

Tuba, Eva; Tuba, Milan; Simian, Dana; Jovanović, Raka

doi:10.1007/978-3-319-59108-7_23

Cited by 23 publications

(4 citation statements)

References 25 publications

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“…An obvious and expected line of work has dealt with a direct attempt to improve JPEG rate-distortion performance [4,5,6,7,8,9,10,11,12,13]. Papers offering such a treatment differ mainly in the optimization strategy adopted, as the techniques used include simulated annealing [4,13], coordinate-descent [5,7,8,10], dynamic programming [6], genetic and evolutionary algorithms [9], exhaustive separable search [11] and a swarm intelligence method [12]. Note that all these methods employ derivative-free optimization strategies due to the complex endto-end function being treated.…”

Section: Related Workmentioning

confidence: 99%

“…Previous work from the early 90's and since have already identified the potential in better designing the JPEG quantization tables, considering various approaches [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. The main effort has been directed to rate-distortion performance improvement, using derivativefree optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

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The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Luo

Talebi

Yang

et al. 2020

Preprint

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Default Optimized Default Optimized PSNR = 30.24dB PSNR = 30.62dB "Dragonfly" [0.16] "Bee Eater" [0.25] BPP = 3.06 BPP = 2.30 BPP = 1.706 BPP = 1.680 Fig. 1: We offer an optimization of JPEG's quantization tables for improved ratedistortion or rate-accuracy performance. The above are sample results showing (i) a 17% reduction in file size while maintaining the same quality, and (ii), a corrected classification while saving 1.5% in file size.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Luo

Talebi

Yang

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Satellite images plays an important part in fields such as weather forecast and land cover classification, demand for fact transmission is necessary for fast acquisition of image thus compression is utilized [7]. In compression quantization is a lossy process used to reduce the information but eliminating or minimize the quantity of high spatial frequencies [8][9][10][11][12][13][14][15] in a DCT applied 8x8 Macro block [16][17][18][19][20][21]. Khowaja et.…”

Section: Introductionmentioning

confidence: 99%

Exhaustive Crisp Parameter Modification in Quantization Table for Effective Image Compression

Siddique

Qadri

Mohy-Ud-Din

2020

Mehran Univ. res. j. eng. technol.

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In recent times, transmission of information over wireless channels is increasing at an exponential rate. Internet is major source of information; it can be in the form of video or images which alone size up to 72% of the global traffic. In order to tackle such immense data, available channel may not be enough for transmission or reception, in this regard it is imperative to use efficient compressions techniques to reduce its size. Compressed image quality depends on the Quantization Table performed after spatial transformation like Discrete Cosine Transform. Size of a raw image captured by Digital Single-Lens Reflex, having all of its traits can easily exceed 20 Mega Bytes. In proposed compression algorithm, a Crisp parameter p modification step is introduced for effective compression of an image by utilizing standard Joint Picture Expert Group Quantization Table as a baseline model. After implementation of the proposed algorithm, Mean Opinion Score is obtained from the masses through an online survey and it provide the scores of 47.633 at p = 1, 62.74 at p = 8, and 83.252 at p = 16. According to Mean opinion score, best trade-off between quality and size of an image is between the values of p ranges from 11-20, this is also proved by Mean Squared Error and Peak Signal to Noise Ratio, as their ranges are 0.00038-0.000301 and 34.09-34.92 dB respectively. Compression Ratio which is from 6.49-5.76 is also acceptable for the given range.

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“…Similar optimization work has been produced with other codecs; mainly JPEG, for which quantization tables have been optimized for different tasks using various methods [3][4] [5] [6], and JPEG2000, which has had its large parameter space explored with the use of genetic algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Adapting JPEG XS gains and priorities to tasks and contents

Brummer,

De Vleeschouwer

2020

Preprint

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Most current research in the domain of image compression focuses solely on achieving state of the art compression ratio, but that is not always usable in today's workflow due to the constraints on computing resources.Constant market requirements for a low-complexity image codec have led to the recent development and standardization of a lightweight image codec named JPEG XS.In this work we show that JPEG XS compression can be adapted to a specific given task and content, such as preserving visual quality on desktop content or maintaining high accuracy in neural network segmentation tasks, by optimizing its gain and priority parameters using the covariance matrix adaptation evolution strategy.

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JPEG Quantization Table Optimization by Guided Fireworks Algorithm

Cited by 23 publications

References 25 publications

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Exhaustive Crisp Parameter Modification in Quantization Table for Effective Image Compression

Adapting JPEG XS gains and priorities to tasks and contents

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