Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction
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Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction Saleh Ali Alshehri 1 Received: 11 October 2019 / Revised: 22 April 2020 / Accepted: 5 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Video compression . Image processing . Discrete cosinetransform . Frame redundancy
* Saleh Ali Alshehri [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications 15
1 Introduction It is expected that multimedia video content will consume more than 70% of the communication bandwidth by 2021 [4]. This expectation is obvious considering the increasing demand on social media applications [17]. This increased desire necessitates an increase in content storage capacity and a wider communication bandwidth. Fulfilling these requirements is not always possible. For this reason, there are many studies related to image and video compression [5, 8, 11, 12, 14, 16]. Since any video clip consists of many images, what applies for an image applies for video clips as well, in addition to temporal information as motions in video c
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