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International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(4): 366-372

Published online December 25, 2022

https://doi.org/10.5391/IJFIS.2022.22.4.366

© The Korean Institute of Intelligent Systems

MB-FL: Macro-Block Fuzzy Logic for Video Compression in Multimedia Applications

B. Veerasamy and C. M. Sangeetha

Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Correspondence to :
B. Veerasamy (veerasamypapers2021@gmail.com)

Received: January 10, 2022; Revised: August 10, 2022; Accepted: December 11, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods to reduce the iterations needed to process a given video stream are referred to as video compression techniques. Videos consume a large amount of storage space on computational systems or handheld devices, and video compression is also used to lower the dimensionality of data owing to constraints on storage resources. In this study, we propose a macro block-based fuzzy logic video compression (MB-FL) algorithm. The proposed approach uses a fuzzy-based search to maintain pixel resolution, which is ideal for real time streaming media and thus increases peak signal-to-noise ratio (PSNR) and subjective quality. Compression is performed on repeated frames of data files via complex equations, and the repeating patterns are then substituted with smaller data or coding fragments. Owing bandwidth limitations, compression is often necessary to transmit and receive content over network connections. Using a fuzzy membership function, the multiscale aspect of our method evaluates the connection of individual components in the current frame to those in the reference frame. The results of an experimental evaluation show that the proposed approach significantly compressed files using a fuzzy-based search. We compared the performance of MB-FL with that of existing models to measure the quality of compressed video stream.

Keywords: Computational macro block based fuzzy logic video compression (MB-FL), Membership function (MF), Sum of absolute differences (SAD)

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(4): 366-372

Published online December 25, 2022 https://doi.org/10.5391/IJFIS.2022.22.4.366

Copyright © The Korean Institute of Intelligent Systems.

MB-FL: Macro-Block Fuzzy Logic for Video Compression in Multimedia Applications

B. Veerasamy and C. M. Sangeetha

Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Correspondence to:B. Veerasamy (veerasamypapers2021@gmail.com)

Received: January 10, 2022; Revised: August 10, 2022; Accepted: December 11, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Methods to reduce the iterations needed to process a given video stream are referred to as video compression techniques. Videos consume a large amount of storage space on computational systems or handheld devices, and video compression is also used to lower the dimensionality of data owing to constraints on storage resources. In this study, we propose a macro block-based fuzzy logic video compression (MB-FL) algorithm. The proposed approach uses a fuzzy-based search to maintain pixel resolution, which is ideal for real time streaming media and thus increases peak signal-to-noise ratio (PSNR) and subjective quality. Compression is performed on repeated frames of data files via complex equations, and the repeating patterns are then substituted with smaller data or coding fragments. Owing bandwidth limitations, compression is often necessary to transmit and receive content over network connections. Using a fuzzy membership function, the multiscale aspect of our method evaluates the connection of individual components in the current frame to those in the reference frame. The results of an experimental evaluation show that the proposed approach significantly compressed files using a fuzzy-based search. We compared the performance of MB-FL with that of existing models to measure the quality of compressed video stream.

Keywords: Computational macro block based fuzzy logic video compression (MB-FL), Membership function (MF), Sum of absolute differences (SAD)

Fig 1.

Figure 1.

Process of compression and decompression.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 2.

Figure 2.

Proposed MB-FL model.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 3.

Figure 3.

Representation of I-frame, B-frame, and P-frame in video compression.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 4.

Figure 4.

(a) Predicted frame, (b) residual frame, (c) motion vector, (d) predicted frame MB-FL, (e) residual frame MB-FL, and (f) motion vector MB-FL with area p = 16, block size b = 16.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 5.

Figure 5.

(p = 4, b = 4) PSNR, MSE and SSIM comparison.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 6.

Figure 6.

(p = 8, b = 8) PSNR, MSE and SSIM comparison.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Fig 7.

Figure 7.

(p = 16, b = 16) PSNR, MSE and SSIM comparison.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 366-372https://doi.org/10.5391/IJFIS.2022.22.4.366

Table 1 . PSNR comparison table (tajmahal.mp4).

Approachp = 4, b = 4p = 8, b = 8p = 16, b = 16
HS65.423766.785465.5284
DS69.324868.538768.1275
FS67.589667.687966.4691
Proposed MB-FL70.496869.897169.6287

Table 2 . Comparison of PSNR, MSE and SSIM (p = 4, b = 4).

ApproachPSNRMSESSIM
HS64.423740.5546.7
DS68.324855.7943.98
FS67.589657.3235.6
Proposed MB-FL70.496858.8929.5

Table 3 . Comparison of PSNR, MSE and SSIM (p = 8, b = 8).

ApproachPSNRMSESSIM
HS65.785439.7844.5
DS67.538740.8340.2
FS66.687955.3235.8
Proposed MB-FL69.897159.8928.5

Table 4 . Comparison of PSNR, MSE and SSIM (p = 16, b = 16).

ApproachPSNRMSESSIM
HS65.528444.546.7
DS68.127540.7943.6
FS65.469157.3235.3
Proposed MB-FL69.628759.8930.8