An efficient versatile video coding motion estimation hardware

Ahmad, Waqar; Mahdavi, Hossein; Hamzaoglu, Ilker

doi:10.1007/s11554-023-01402-8

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Video information transmission accounts for the majority of web traffic these days. With the extensive mobile phone usage pattern worldwide, video information is commonly used for efficient gadgets and entertainment stages that support people's employment and existence from unique perspectives [1,2]. In addition to the pervasive video commitment, prevalent video quality norms, such as 4K UHD and VR 360, turned out to be all the more generally accessible, making superior video compression much more critical.…”

Section: Introductionmentioning

confidence: 99%

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

Mudhavath,

Kumar,

Yadav

et al. 2024

Preprint

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The issues of finding a suitable loss function for perceived similarity and enhancing perceptual quality in substantially compressed videos still need to be resolved. The LIGAN-Conv-LSTM is a convolutional long-short-term memory system that integrates a loss-initialised generative adversarial network. This system was developed to address the challenge of defining unified training objectives that improve both rough and smooth content. The goal is to enhance compression quality by employing advanced feature selection and prediction techniques. The study begins by presenting a comprehensive examination of multimedia, emphasising its diverse attributes, encompassing retention, awareness, conversation, and knowledge. The research employs Shape Local Binary Texture (SLBT), Overlapped Segmentation-based Independent Component Analysis (OS-ICA), and Scale-Invariant Feature Transform (SIFT) as approaches to extract useful features. Furthermore, this research acknowledges the necessity for enhanced motion estimates. To address this, a hybrid technique is proposed to integrate the most advantageous elements of coyote and tuna swarm optimisation. The objective is to enhance the accessibility of feature selection, a crucial factor in improving classifier performance. The primary contribution of the research involves replacing standard motion estimating algorithms with a Conv-LSTM network, enabling the prediction of concise latent representations for future frames. The study's results emphasise the efficacy of the proposed hybrid search algorithm in achieving a high level of matching accuracy, as demonstrated by testing on a representative collection of video clips. The planned task's accuracy level is 97.73, demonstrating its cost-effective productivity.

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Section: Introductionmentioning

confidence: 99%

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

Mudhavath,

Kumar,

Yadav

et al. 2024

Preprint

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SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time

Tasyurek

2024

J Real-Time Image Proc

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Detecting and repairing road defects is crucial for road safety, vehicle maintenance, and enhancing tourism on well-maintained roads. However, monitoring all roads by vehicle incurs high costs. With the widespread use of remote sensing technologies, high-resolution satellite images offer a cost-effective alternative. This study proposes a new technique, SDPH, for automated detection of damaged roads from vast, high-resolution satellite images. In the SDPH technique, satellite images are organized in a pyramid grid file system, allowing deep learning methods to efficiently process them. The images, generated as $$256\times 256$$ 256 × 256 dimensions, are stored in a directory with explicit location information. The SDPH technique employs a two-stage object detection models, utilizing classical and modified RCNNv3, YOLOv5, and YOLOv8. Classical RCNNv3, YOLOv5, and YOLOv8 and modified RCNNv3, YOLOv5, and YOLOv8 in the first stage for identifying roads, achieving f1 scores of 0.743, 0.716, 0.710, 0.955, 0.958, and 0.954, respectively. When the YOLOv5, with the highest f1 score, was fed to the second stage; modified RCNNv3, YOLOv5, and YOLOv8 detected road defects, achieving f1 scores of 0.957,0.971 and 0.964 in the second process. When the same CNN model was used for road and road defect detection in the proposed SDPH model, classical RCNNv3, improved RCNNv3, classical YOLOv5, improved YOLOv5, classical YOLOv8, improved RCNNv8 achieved micro f1 scores of 0.752, 0.956, 0.726, 0.969, 0.720 and 0.965, respectively. In addition, these models processed 11, 10, 33, 31, 37, and 36 FPS images by performing both stage operations, respectively. Evaluations on geotiff satellite images from Kayseri Metropolitan Municipality, ranging between 20 and 40 gigabytes, demonstrated the efficiency of the SDPH technique. Notably, the modified YOLOv5 outperformed, detecting paths and defects in 0.032 s with the micro f1 score of 0.969. Fine-tuning on TileCache enhanced f1 scores and reduced computational costs across all models.

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Hardware implementation of iterative method for enhanced affine motion estimation in Versatile video coding

Hong,

Dong,

Pang

et al. 2024

J Real-Time Image Proc

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An efficient versatile video coding motion estimation hardware

Cited by 5 publications

References 27 publications

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time

Hardware implementation of iterative method for enhanced affine motion estimation in Versatile video coding

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