Arman Process Journal (APJ)
Quarterly Journal of Information and Communication Technology ​

Wireless Portable Video on Demand System and Pyramidal VQ Decoder Method with Low Power Video Rate Survey

Document Type : Review Paper

Author

Sepideh Gohari

Computer Engineering Department, Islamic Azad University , North Tehran Branch ,Tehran , Iran

Abstract
The portable video-on-demand system is a video compression system for transmitting video in a wireless network, it is sent in smaller packets and uses Wi-Fi and Bluetooth standards to connect to mobile devices and tablets at any time and place. There is a method for transmitting video in a portable wireless network. Here, we use the VQ pyramid decoding method, a video compression method that we used to transmit video in wireless networks with limited bandwidth. This method is able to reduce the amount of data and preserve In this method, the video quality is divided into two small frames. This algorithm uses a set of quantized codes to select the closest code to each block of the frame, and then this code is used as a representative of the block in the frame. The method instead of transmitting all the block information, only the block representative code information is transmitted, which reduces the data volume. In fact, VQ pyramid decoding is an efficient method for video compression and transmission in wireless networks, which reduces the data volume and maintains the video quality. It provides the possibility of transmitting and watching high-quality video in a network with limited bandwidth. Overall, literature reviews in this field analyze and examine these techniques and their applications for optimizing video systems, and they aim to find the best methods for compressing videos with high quality at low bit rates.

Highlights

[1] Pennebaker WB, Mitchell JL. JPEG: Still image data compression standard. Springer Science & Business Media; 1992 Dec 31.

[2] Rubinstein R, Bruckstein AM, Elad M. Dictionaries for sparse representation modeling. Proceedings of the IEEE. 2010 Apr 22;98(6):1045-57.

[3]Meng TH, Tsern EK, Hung AC, Hemami SS, Gordon BM. Video compression for wireless communications. Wireless Personal Communications: Trends and Challenges. 1994:101-17.

[4] Li S, Kang X, Fang L, Hu J, Yin H. Pixel-level image fusion: A survey of the state of the art. information Fusion. 2017 Jan 1;33:100-12.

[5] Sheltami T, Musaddiq M, Shakshuki E. Data compression techniques in wireless sensor networks. Future Generation Computer Systems. 2016 Nov 1;64:151-62.

[6]Mammeri A, Hadjou B, Khoumsi A. A survey of image compression algorithms for visual sensor networks. International Scholarly Research Notices. 2012;2012.

[7]Shafi R, Shuai W, Younus MU. 360-degree video streaming: A survey of the state of the art. Symmetry. 2020 Sep 10;12(9):1491.

[8]Xu R, Razavi S, Zheng R. Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques. IEEE Communications Surveys & Tutorials. 2023 Oct 10.

[9] Min X, Duan H, Sun W, Zhu Y, Zhai G. Perceptual video quality assessment: A survey. arXiv preprint arXiv:2402.03413. 2024 Feb 5..

[10] Min X, Duan H, Sun W, Zhu Y, Zhai G. Perceptual video quality assessment: A survey. arXiv preprint arXiv:2402.03413. 2024 Feb 5.

[11] Madhusudana PC, Birkbeck N, Wang Y, Adsumilli B, Bovik AC. Conviqt: Contrastive video quality estimator. IEEE Transactions on Image Processing. 2023 Sep 7.

[12]Bokhari SM, Nix AR, Bull DR. Rate-distortion-optimized video transmission using pyramid vector quantization. IEEE transactions on image processing. 2012 Mar 21;21(8):3560-72.

[13]. Saha A, Pentapati SK, Shang Z, Pahwa R, Chen B, Gedik HE, Mishra S, Bovik AC. Perceptual video quality assessment: The journey continues!. Frontiers in Signal Processing. 2023 Jun 27;3:1193523.

[14] Chen Y, Wu K, Zhang Q. From QoS to QoE: A tutorial on video quality assessment. IEEE Communications Surveys & Tutorials. 2014 Oct 22;17(2):1126-65.

[15] Kougioumtzidis G, Poulkov V, Zaharis ZD, Lazaridis PI. A survey on multimedia services QoE assessment and machine learning-based prediction. IEEE Access. 2022 Feb 7;10:19507-38.

 [16] Jung B, Burleson WP. VLSI array architectures for pyramid vector quantization. Journal of VLSI signal processing systems for signal, image and video technology. 1998 Feb;18:141-54.

[17] Russek P, Wiatr K. Rekonfigurowalny kwantyzator wektorowy do kodowania obrazów w czasie rzeczywistym. Kwartalnik Elektroniki i Telekomunikacji. 2003;49(3):355-72..

[18] Russek P, Wiatr K. Zastosowanie techniki rekonfigurowalności sprzętowej przy budowie systemów kompresji obrazu. Automatyka/Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. 2003;7(3):767-78.

[19]Alhayani BS, Hamid N, Almukhtar FH, Alkawak OA, Mahajan HB, Kwekha-Rashid AS, İlhan H, Marhoon HA, Mohammed HJ, Chaloob IZ, Alkhayyat A. Optimized video internet of things using elliptic curve cryptography based encryption and decryption. Computers and Electrical Engineering. 2022 Jul 1;101:108022.

[20]Sheltami T, Musaddiq M, Shakshuki E. Data compression techniques in wireless sensor networks. Future Generation Computer Systems. 2016 Nov 1;64:151-62.

[21] Huang CJ, Cheng HW, Lien YH, Jian ME. A survey on video streaming for next-generation vehicular networks. Electronics. 2024 Feb 4;13(3):649.

[22] van der Hooft J, Amirpour H, Vega MT, Sanchez Y, Schatz R, Schierl T, Timmerer C. A tutorial on immersive video delivery: From omnidirectional video to holography. IEEE Communications Surveys & Tutorials. 2023 Mar 30;25(2):1336-75.

Keywords

VQ Decoder
Encoder
2D Decoder
Low Power Video Rate
PVQ Vector
Chip Architecture
[1] Pennebaker WB, Mitchell JL. JPEG: Still image data compression standard. Springer Science & Business Media; 1992 Dec 31.
[2] Rubinstein R, Bruckstein AM, Elad M. Dictionaries for sparse representation modeling. Proceedings of the IEEE. 2010 Apr 22;98(6):1045-57.
[3]Meng TH, Tsern EK, Hung AC, Hemami SS, Gordon BM. Video compression for wireless communications. Wireless Personal Communications: Trends and Challenges. 1994:101-17.
[4] Li S, Kang X, Fang L, Hu J, Yin H. Pixel-level image fusion: A survey of the state of the art. information Fusion. 2017 Jan 1;33:100-12.
[5] Sheltami T, Musaddiq M, Shakshuki E. Data compression techniques in wireless sensor networks. Future Generation Computer Systems. 2016 Nov 1;64:151-62.
[6]Mammeri A, Hadjou B, Khoumsi A. A survey of image compression algorithms for visual sensor networks. International Scholarly Research Notices. 2012;2012.
[7]Shafi R, Shuai W, Younus MU. 360-degree video streaming: A survey of the state of the art. Symmetry. 2020 Sep 10;12(9):1491.
[8]Xu R, Razavi S, Zheng R. Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques. IEEE Communications Surveys & Tutorials. 2023 Oct 10.
[9] Min X, Duan H, Sun W, Zhu Y, Zhai G. Perceptual video quality assessment: A survey. arXiv preprint arXiv:2402.03413. 2024 Feb 5..
[10] Min X, Duan H, Sun W, Zhu Y, Zhai G. Perceptual video quality assessment: A survey. arXiv preprint arXiv:2402.03413. 2024 Feb 5.
[11] Madhusudana PC, Birkbeck N, Wang Y, Adsumilli B, Bovik AC. Conviqt: Contrastive video quality estimator. IEEE Transactions on Image Processing. 2023 Sep 7.
[12]Bokhari SM, Nix AR, Bull DR. Rate-distortion-optimized video transmission using pyramid vector quantization. IEEE transactions on image processing. 2012 Mar 21;21(8):3560-72.
[13]. Saha A, Pentapati SK, Shang Z, Pahwa R, Chen B, Gedik HE, Mishra S, Bovik AC. Perceptual video quality assessment: The journey continues!. Frontiers in Signal Processing. 2023 Jun 27;3:1193523.
[14] Chen Y, Wu K, Zhang Q. From QoS to QoE: A tutorial on video quality assessment. IEEE Communications Surveys & Tutorials. 2014 Oct 22;17(2):1126-65.
[15] Kougioumtzidis G, Poulkov V, Zaharis ZD, Lazaridis PI. A survey on multimedia services QoE assessment and machine learning-based prediction. IEEE Access. 2022 Feb 7;10:19507-38.
 [16] Jung B, Burleson WP. VLSI array architectures for pyramid vector quantization. Journal of VLSI signal processing systems for signal, image and video technology. 1998 Feb;18:141-54.
[17] Russek P, Wiatr K. Rekonfigurowalny kwantyzator wektorowy do kodowania obrazów w czasie rzeczywistym. Kwartalnik Elektroniki i Telekomunikacji. 2003;49(3):355-72..
[18] Russek P, Wiatr K. Zastosowanie techniki rekonfigurowalności sprzętowej przy budowie systemów kompresji obrazu. Automatyka/Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. 2003;7(3):767-78.
[19]Alhayani BS, Hamid N, Almukhtar FH, Alkawak OA, Mahajan HB, Kwekha-Rashid AS, İlhan H, Marhoon HA, Mohammed HJ, Chaloob IZ, Alkhayyat A. Optimized video internet of things using elliptic curve cryptography based encryption and decryption. Computers and Electrical Engineering. 2022 Jul 1;101:108022.
[20]Sheltami T, Musaddiq M, Shakshuki E. Data compression techniques in wireless sensor networks. Future Generation Computer Systems. 2016 Nov 1;64:151-62.
[21] Huang CJ, Cheng HW, Lien YH, Jian ME. A survey on video streaming for next-generation vehicular networks. Electronics. 2024 Feb 4;13(3):649.
[22] van der Hooft J, Amirpour H, Vega MT, Sanchez Y, Schatz R, Schierl T, Timmerer C. A tutorial on immersive video delivery: From omnidirectional video to holography. IEEE Communications Surveys & Tutorials. 2023 Mar 30;25(2):1336-75.

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