Review and evaluation of color spaces for image/video compression

Kahu, Samruddhi Y.; Raut, Rajesh B.; Bhurchandi, Kishor M.

doi:10.1002/col.22291

Cited by 47 publications

(25 citation statements)

References 84 publications

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“…The component a of the Lab represents values on an axis that vary between red (positive values) and green (negative values), and in component b the values vary between yellow (positive values) and blue (negative values). As the apple fruits strongly emphasize the red color and both the leaves and ground coverage tend to green, the differentiation on the same axis facilitates the division of these objects in the image (Kahu, S.Y., Raut, R.B., and Bhurchandi, K.M., 2019).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

et al. 2021

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This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina's Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil's apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I 1 , I 2 , I 3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I" 2 , I" 3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I" 2 , I" 3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed

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Section: Discussionmentioning

confidence: 99%

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

et al. 2021

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“…It requires some predefined sets of conversion formulae to covert the RGB color space to IPT color space. The detail description of conversion step is explained in (Kahu et al, 2019). SSIM operates separately on each channel in the IPT color space.…”

Section: Structural Similarity Index Ssimlptmentioning

confidence: 99%

A Novel Feature Extraction Dual DCT-DWT Image Watermarking Combined with Chaos-Based Cryptosystem

Rinki¹,

Verma²,

Choudhury³

et al. 2021

Journal of Computer Science

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As a result of the emerging technologies such as Internet-of-Things (IoT), data analysis and automation in many aspects of identity, watermarking has become increasingly important. A majority of prior systems focused on watermark embedding and recovery techniques for binary and grey-scale images in one specified region of the cover image, making the system less dependable. This study develops a novel feature extraction based blind cryptographic watermarking mechanism which provides multi-layered protection to digital data to significantly improve the watermarking system's characteristics such as robustness, visible quality and security. A hybrid algorithm is proposed by combining DCT and multilevel DWT transformation technique to provide copyright protection and authentication to digital image over the internet. Unlike the previous model here both the host and watermark images are considered colored. Prior to embedding, a layer wise encryption is performed on watermark using Chaosbased cryptosystem to enhance the security of proposed system. It is processed by applying confusion and diffusion process on individual RBG (Red, Green and Blue) components of watermark image. Then the cover image and watermark image are directed by DCT coefficient and multiregion Discrete wavelet transformation to embed the watermark in both high and low sub band of cover image. Thus, by embedding the watermark twice in low and high regions of the cover image, the algorithm's robustness is considerably improved. For better imperceptibility, the proposed method also applies novel perception using SSIMlPT metric to extract the similar features between cover image and watermark image. This is done by dividing the cover image and watermark image into non overlapping blocks of size 8x8 respectively. Now, the best match blocks of cover image are chosen for further embedding a watermark in both low-frequency and high-frequency regions of cover image. In addition, the use of non-consecutive blocks of pixels to contain the watermark makes the system more resistant to a range of attacks. In an experimental scenario, the approach is evaluated using a variety of quality criteria and watermark removal attacks. In comparison to other existing systems, the results suggest that the model can allow invisible watermarking as well as excellent attack resistance.

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“…For one dimensional, signals are split into two parts: high and low frequencies. The low pass and high pass filter of DWT is expressed as [40,[59][60][61][62]:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Performance analysis of compression algorithms for information security: A Review

Sharma

Batra

2018

ICST Transactions on Scalable Information Systems

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Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques:-lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like-file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.

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Review and evaluation of color spaces for image/video compression

Cited by 47 publications

References 84 publications

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

Evaluating the Performance of a Semi-Automatic Apple Fruit Detection in a High-Density Orchard System Using Low-Cost Digital RGB Imaging Sensor

A Novel Feature Extraction Dual DCT-DWT Image Watermarking Combined with Chaos-Based Cryptosystem

Performance analysis of compression algorithms for information security: A Review

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