Enhancing the Quality of Medical Images Containing Blur Combined with Noise Pair

Karimzadeh, Hojatallah; Ramezanpour, Mohammadreza

doi:10.5815/ijigsp.2015.11.03

Cited by 2 publications

(7 citation statements)

References 5 publications

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“…The following test sequences validate our results and compare them with various methods, as discussed in Standard HEVC decoded frame, Kim et al [17], Chen et al [22], Norkin et al [23], Karimzadeh et al [25], Wang et al [28]. The details of test sequences are as shown in Tab.…”

Section: Resultssupporting

confidence: 57%

“…In the HEVC method, blocking artifacts are observed at low bitrates due to its large block size, different partition blocks, different chroma, and luma components [22][23][24][25][26][27][28][29][30][31]. De-blocking Filter (DBF) used for the chroma component is simple.…”

Section: Motivation and Contributionmentioning

confidence: 99%

“…However, its performance is low in the chroma components as compared to the luma component. Thus, artifacts arising due to the chroma component still needs to be explored [25][26][27][28][29][30][31][32].…”

Section: Motivation and Contributionmentioning

confidence: 99%

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Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

Gandam¹,

Sidhu²

2021

Computers, Materials &Amp; Continua

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In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding (HEVC) frames and improves its subjective visual quality in multimedia services over communication networks. However, on faster processing of the complex videos at a low bitrate, some visible artifacts considerably degrade the picture quality. In this paper, we proposed a four-step fuzzy based adaptive deblocking filter selection technique. The proposed method removes the quantization noise, blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate. We have considered Y (luma), U (chromablue), and V (chroma-red) components parallelly. Finally, we have developed a fuzzy system to detect blocking artifacts and use adaptive filters as per requirement in all four quadrants, namely up 45 • , down 45 • , up 135 • , and down 135 • across horizontal and vertical block boundaries. In this context, experimentation is done on a wide variety of videos. An objective and subjective analysis is carried out with MATLAB software and Human Visual System (HVS). The proposed method substantially outperforms existing postprocessing deblocking techniques in terms of YPSNR and BD_rate. In the proposed method, we achieved 0.32-0.97 dB values of YPSNR. Our method achieved a BD_rate of +1.69% for the luma component, −0.18% (U) and −1.99% (V) for chroma components, respectively, with respect to the stateof-the-art methods. The proposed method proves to have low computational complexity and has better parallel processing, hence suitable for a real-time system in the near future.

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Section: Resultssupporting

confidence: 57%

Section: Motivation and Contributionmentioning

confidence: 99%

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Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

Gandam¹,

Sidhu²

2021

Computers, Materials &Amp; Continua

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“…Then we define 4 × 4 block-based classifications for Cl I and Cl R,I by simply replacing Y(i, j) and Y(k 1 , k 2 ) by Y (B(i, j)) and Y(B(k 1 , k 2 )) in (6) and (7). Finally, we apply (21) with those 4 × 4 block-based classifications instead of Cl R,I and Cl I .…”

Section: Discussion Of Complexity/implementation Aspectsmentioning

confidence: 99%

“…In the currently deployed HEVC video standard, two in-loop filters are included. The first one is the deblocking filter [5][6][7]. Here, low-pass filters adaptively smooth boundary samples in order to suppress blocking artifacts.…”

Section: Introductionmentioning

confidence: 99%

Extended multiple feature-based classifications for adaptive loop filtering

Erfurt

Lim

Schwarz

et al. 2019

SIP

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Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algorithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifications for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Furthermore, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm.

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Enhancing the Quality of Medical Images Containing Blur Combined with Noise Pair

Cited by 2 publications

References 5 publications

Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks

Extended multiple feature-based classifications for adaptive loop filtering

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