Multi-Level Optimization in Encoding to Balance Video Compression and Retention of 8K Resolution

N, Murthy S V; Sujatha, B.

doi:10.1016/j.pisc.2016.04.069

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Every square in the CF, the best coordinating block is recognized in the hunting space of the past frame to limit the Mean-Square-Error (MSE) among macro blocks. The key test is the assessment of MSE is very tedious (Kwon et al 2020, Murthy et al 2016and Mukherjee et al 2018. Thus, BM algorithm for ME is measured as an advancement issue and it has an objective to find the better coordinating macro square for an objective (Lee et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Diamond Search Optimization-Based Technique for Motion Estimation in Video Compression

Ravuri¹

2023

International Journal of E-Collaboration

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In video compression procedure, movement estimation is one of the key segments due to its high computation unpredictability in finding the movement vectors between the frames. The purpose of movement estimation is to diminish the storage space, data transfer capacity, and transmission cost for transmission of video in numerous mixed media administration applications by decreasing the redundancies while preserving the better quality of the video. Each algorithm has its own benefits and culpabilities. Among these, block-based movement estimation calculations are most powerful and adaptable. In this paper, diamond search-hybrid teaching and learning-based optimization (DS-HTLBO) has been proposed for motion estimation. The performance of the proposed DS-HTLBO method is analyzed by considering different performance evaluation parameters such as peak signal-to-noise ratio, mean square error, and compression ratio. The comparative outcomes reveal that the proposed DS-HTLBO method outperformed in terms of PSNR of 41% and CR of 5.47% with other DS, 4SS, and NTSS methods.

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Section: Introductionmentioning

confidence: 99%

Diamond Search Optimization-Based Technique for Motion Estimation in Video Compression

Ravuri¹

2023

International Journal of E-Collaboration

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“…As seen below, the CDR is calculated. CDR=areaCup/areaDisc It is glaucomatous if the CDR [26] is greater than or equal to 0.6; else, it is not. The first stage in performing automatic glaucoma detection, in accordance with this and what was learned from the literature review, is to get the segmentation of the disc and the cup, as shown in Figure 5 Load the normal fundus images and convert them to grayscale and use a low-pass filter to the grayscale image and use gaussian function from the skimage.filters.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Glaucoma Screening from Digital Retinal Fundus Images using an Ensemble Deep Learning based Approach

Santhosh

Babu

2022

Preprint

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A condition known as glaucoma, which affects the optic nerve, can cause visual loss that is either partial or total. Therefore, there is a critical need for glaucoma screening at an early age. The symptoms related to glaucoma are not noticeable until severe stages of this illness and they appear when the patient has already lost a significant part of his eyesight. The majority of earlier diagnosis strategies rely on handmade feature engineering. The use of fundus images for the early detection of eye disorders is of tremendous clinical value. Deep learning is becoming more and more common in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis, and image synthesis, due to its potent performance. Convolutional neural networks (CNNs) have recently been utilized in the field of ophthalmology to identify specific eye ailments, including glaucoma. They have demonstrated good performance in the early detection of several diseases. The method described in this paper uses densely connected neural networks with numerous layers that were initially trained on ImageNet using the fundus dataset that is readily available. We can be cautiously optimistic about the efficacy of our classification model for the early identification of glaucoma given the accuracy of almost 97 that was achieved.

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“…From the viewpoint of a video file, the real object of compression is to minimise the overall file's size for storage optimisation. However, with the increasing demands of digital entertainment, the researchers have been focused more on the data availability with guaranteeing a superior data quality [1]. Therefore, H.265 has appeared as latest compression standards to fulfil these requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Usage of H.265 has also opened the arena of high‐resolution visual display, i.e. 8 K ultra‐high definition (UHD) as it can offer an efficient compression performance as well as minimising the utilisation of channel capacity [1, 11]. The usage of H.265 has slowly gained pace and is used by the commercial product design as well as various types of service providers targeting to the 4 K broadcasting of television channels [12, 13].…”

Section: Introductionmentioning

confidence: 99%

RHEVC intra‐prediction mode

2019

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Although the high‐efficiency video codec plays a leading role among the video coding standards, it consumes a lot of memory resources and time processing due to its superior video quality and coding performance. The computation algorithm of rate distortion optimisation is one of the reasons behind the encoder's bottleneck. Therefore, a new technique has been introduced here based on the entropy threshold and size of coding unit, which reduces the coding time and bit rate to be more suitable for real‐time processing. According to the obtained results, the encoding time has been enhanced by 56.596% with a reduction in the bit rate reached to 74% on average.

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Multi-Level Optimization in Encoding to Balance Video Compression and Retention of 8K Resolution

Cited by 9 publications

References 13 publications

Diamond Search Optimization-Based Technique for Motion Estimation in Video Compression

Diamond Search Optimization-Based Technique for Motion Estimation in Video Compression

Real-Time Glaucoma Screening from Digital Retinal Fundus Images using an Ensemble Deep Learning based Approach

RHEVC intra‐prediction mode

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