Abstract
The need of human beings for better social media applications has increased tremendously. This increase has necessitated the need for a digital system with a larger storage capacity and more processing power. However, an increase in multimedia content size reduces the overall processing performance. This occurs because the process of storing and retrieving large files affects the execution time. Therefore, it is extremely important to reduce the multimedia content size. This reduction can be achieved by image and video compression. There are two types of image or video compression: lossy and lossless. In the latter compression, the decompressed image is an exact copy of the original image, while in the former compression, the original and the decompressed image differ from each other. Lossless compression is needed when every pixel matters. This can be found in autoimage processing applications. On the other hand, lossy compression is used in applications that are based on human visual system perception. In these applications, not every single pixel is important; rather, the overall image quality is important. Many video compression algorithms have been proposed. However, the balance between compression rate and video quality still needs further investigation. The algorithm developed in this research focuses on this balance. The proposed algorithm exhibits diversity of compression stages used for each type of information such as elimination of redundant and semi redundant frames, elimination by manipulating consecutive XORed frames, reducing the discrete cosine transform coefficients based on the wanted accuracy and compression ratio. Neural network is used to further reduce the frame size. The proposed method is a lossy compression type, but it can reach the near-lossless type in terms of image quality and compression ratio with comparable execution time.
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References
Ahmed N (1991) How I came up with the discrete cosine transform. Digital Signal Processing 1(1):4–5
Al-Ani M, Hammouri T (2011) Video compression algorithm based on frame difference approaches. Int J Soft Computing 2(4):67–79
Alshehri S (2016) Neural network technique for image compression. IET Image Process 10(3):222–226
Bampis C, Li Z, Katsavounidis I, Huang T, Ekanadham C, Bovik A (2018) Towards perceptually optimized end-to-end adaptive video streaming. CoRR, vol. abs/1808.03898, [online] https://arxiv.org/abs/1808.03898.).
Chriqui M, Sinha P (2003) Survey of motion estimation techniques for video compression. Proc. SPIE 4796, low-light-level and real-time imaging systems, components, and applications (https://doi.org/10.1117/12.452142)
Grgic S, Grgic M, Mrak M (2004) Reliability of objective picture quality measures. J Electr Eng 55(1–2):3–1. https://ieeexplore.ieee.org/document/1248017
Jayant N, Johnston J, Safrnnck R (1993) Signal compression based on models of human perception. Proc IEEE 81(10):385–1422
Kavitha S, Anandhi R (2015) A survey of image compression methods for low depth-of-field images and image sequences. Multimed Tools Appl 74:7943–7956
Loukil H, Kacem M, BouhleL M (2012) A new image quality metric using system visual human characteristics. Int J Comp Appl 60(6):32–36. https://doi.org/10.5120/9697-4138
Ma S, Zhang X, Jia C, Zhao Z (2019) Image and video compression with neural networks: a review. IEEE Trans On Circuits Syst Video Technol:1–1
Mrak M, Grgic S, Grgic M (2003) Picture quality measures in image compression systems. Proceedings of the Eurocon 2003 conference, 233-237, Sep. (https://ieeexplore.ieee.org/document/1248017/citations?tabFilter=papers#citations)
Nadenau M, Winkler S, Alleysson D, Kunt M (2003) Human vision models for perceptually optimized image processing – a review. IEEE trans. Image Process 12:58–70
Pathak K, Arjunan R, Acharya V (2019) An innovative lossless image and video compression using revised S transformation. J Adv Res Dynamical Control Syst 11(4):14–24
Pullareddi M, Fathima A (2017) A review on image and video compression standards. Asian J Pharmaceutical Clin Res 10(13):373–377
Raz S, Hashim N, Yahya S, Aziz K, Salleh A, Mohamad N (2014) Different approach of VIDEO compression technique: a study. Int J Sci Eng Appl 3(5):143–149
Rehman M, Sharif M, Raza M (2014) Image compression: a survey. Res J Appl Sci Eng Technol 7(4):656–672
Sandy T, Theint K (2019) Analysis of video compression algorithms using DCT, DWT and FFT. Nat J Parallel Soft Comp 1(1):22–27
Setyaningsih E, Harjoko A (2017) Survey of Hybrid Image Compression Techniques. Int J Electrical Comp Eng 7(4):2206–2214. https://doi.org/10.11591/ijece.v7i3.pp2206-2214 (http://ijece.iaescore.com/index.php/IJECE/article/view/7762)
Sullivan G, Ohm J, Han W, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668
Tawalbeha M, Eardley A, Tawalbeh L (2016) Studying the energy consumption in Mobile devices. Procedia Computer Sci 94:183–189
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error measurement to structural similarity. IEEE Trans Image Processing 13(4):600–612
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Alshehri, S.A. Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction. Multimed Tools Appl 80, 367–381 (2021). https://doi.org/10.1007/s11042-020-09038-7
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DOI: https://doi.org/10.1007/s11042-020-09038-7