A Low-Complexity, Sequential Video Compression Scheme Using Frame Differential Directional Filter Bank Decomposition in CIE <i>La</i>*<i>b</i>* Color Space

Kahu, Samruddhi Y.; Bhurchandi, Kishor M.

doi:10.1109/access.2017.2732001

Cited by 5 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The artifact video metric was used to evaluate the quality of the reconstructed images, with results showing a 60 per cent bitrate saving. Kahu & Bhurchandi, (2017) developed a differential directional filter bank coding scheme to compress videos in a sequential manner. Their proposed algorithm was able to decrease redundancies in the motion compensation stage while their implementation of the adaptive rood pattern search scheme reduces the encoding time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

Motaung¹,

Ogudo²,

Chabalala³

2022

ICONIC

View full text Add to dashboard Cite

DVB (digital video broadcasting) has undergone an enormous paradigm shift, especially through internet streaming that utilizes multiple channels (i.e., secured hypertext transfer protocols). However, due to the limitations of the current communication network infrastructure, video signals need to be compressed before transmission. Whereas most recent research has concentrated and focused on assessing video quality, little to no study has worked on improving the compression processes of digital video signals in lightweight DVB setups. This study provides a video compression strategy (DRL-VC) that employs deep reinforcement learning for learning the suitable parameters used in digital video signal compression. The problem is formulated as a multi-objective one, considering the structural similarity index metric (SSIM), the delay time, and the peak signal-to-noise ratio (PSNR). Based on the findings of the experiments, our proposed scheme increases bitrate savings while at a constant PSNR. Results also show that our scheme performs better than the benchmarked compression schemes. Finally, the root means square error values show a consistent rate across different video streams, indicating the validity of our proposed compression scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

Motaung¹,

Ogudo²,

Chabalala³

2022

ICONIC

View full text Add to dashboard Cite

show abstract

“…Montalvo et al [29] successfully segmented maize crops from weeds using combinations of RGB color components derived from Principal Component Analysis (PCA). However, when the color difference between plants and weeds is not significant, inevitably other processes are needed, such as color space transformation [30][31][32]. Huang et al [33] reported that land cover classification accuracy improved for the GeoEye-1 satellite imagery with an increase in intensity levels of traditional GLCMs, while for the QuickBird satellite it reduced from 91.5% to 90.3% with an increase in intensity levels.…”

Section: Introductionmentioning

confidence: 99%

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

2020

View full text Add to dashboard Cite

Apples are one of the most kind of important fruit in the world. China has been the largest apple producing country. Yield estimating, robot harvesting, precise spraying are important processes for precise planting apples. Image segmentation is an important step in machine vision systems for precision apple planting. In this paper, an apple fruit segmentation algorithm applied in the orchard was studied. The effect of many color features in classifying apple fruit pixels from other pixels was evaluated. Three color features were selected. This color features could effectively distinguish the apple fruit pixels from other pixels. The GLCM (Grey-Level Co-occurrence Matrix) was used to extract texture features. The best distance and orientation parameters for GLCM were found. Nine machine learning algorithms had been used to develop pixel classifiers. The classifier was trained with 100 pixels and tested with 100 pixels. The accuracy of the classifier based on Random Forest reached 0.94. One hundred images of an apple orchard were artificially labeled with apple fruit pixels and other pixels. At the same time, a classifier was used to segment these images. Regression analysis was performed on the results of artificial labeling and classifier classification. The average values of Af (segmentation error), FPR (false positive rate) and FNR (false negative rate) were 0.07, 0.13 and 0.15, respectively. This result showed that this algorithm could segment apple fruit in orchard images effectively. It could provide a reference for precise apple planting management.

show abstract

“…Montalvo et al [29] successfully segmented maize crop from weeds using combinations of RGB color components derived from Principal Component Analysis (PCA). However, when the color difference between plants and weeds is not significant, inevitably other processes are needed, such as color space transformation [30]- [32]. Huang et al [33] reported that land cover classification accuracy improved for the GeoEye-1 satellite imagery with an increase in intensity levels of traditional GLCMs, while for the QuickBird satellite it reduced from 91.5% to 90.3% with an increase in intensity levels.…”

Section: Introductionmentioning

confidence: 99%

Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

Zou

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The segmentation of broccoli seedlings in the crops and weeds co-exist field environment is of great significance for weeding and herbicide spraying. This paper constructed a crop segmentation algorithm with a small training set for discriminating broccoli seedlings from weeds and soil. This algorithm was based on a support vector machine (SVM) combined with color-texture features. Correlation analysis and chi-square tests were used to select 6 features from the 21 color features. Gray-level co-occurrence matrix (GLCM) was used to extract 5 texture features. And each parameter of GLCM had been assessed and optimized by the chi-square test. Linear Discriminant Analysis (LDA) was used to decompose the original dataset in a set of 3 successive orthogonal components. This method selected features more reasonable and gained higher plant segmentation accuracy. When the training sample is greater than 50, the accuracy of the test set could reach 90%. The coefficient of determination (R 2) between the ground truth broccoli seedling area and the segmentation broccoli area was 0.91, and the root-mean-square error (σ) was 0.10. Results demonstrated that the color-texture features were able to effectively segment broccoli seedlings even when there was a significant amount of weeds. INDEX TERMS Pattern recognition, multiple features, support vector machine, broccoli seedling, weed.

show abstract

A Low-Complexity, Sequential Video Compression Scheme Using Frame Differential Directional Filter Bank Decomposition in CIE Lab Color Space

Cited by 5 publications

References 56 publications

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

Contact Info

Product

Resources

About

A Low-Complexity, Sequential Video Compression Scheme Using Frame Differential Directional Filter Bank Decomposition in CIE La*b* Color Space

Cited by 5 publications

References 56 publications

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning

A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning

Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

Contact Info

Product

Resources

About

A Low-Complexity, Sequential Video Compression Scheme Using Frame Differential Directional Filter Bank Decomposition in CIE Lab Color Space