Multimedia Data Compression

2019 ◽  
pp. 107-131
Author(s):  
Sreeparna Banerjee
Keyword(s):  
Data Compression ◽  
Multimedia Data

Information Compression

2010 ◽  
pp. 97-108
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Supply Chain ◽  
Data Compression ◽  
Human Perception ◽  
Large Data ◽  
Multimedia Data ◽  
The Internet ◽  
Information Compression ◽  
Separate Section ◽  
The Web ◽  
Available Resources

If a large data transactions are to happen in the supply chain over the web, the resources would be strained and lead to choking of the network apart from the increased transfer costs. To use the available resources over the internet effectively, the data is often compressed before transfer. This chapter provides the different methods and levels of data compression. A separate section is devoted for multimedia data compression where a certain losses in the data is tolerable during compression due to the limitations of human perception.

A predictive algorithm for multimedia data compression

2012 ◽  
Vol 19 (2) ◽  
pp. 103-115 ◽  
Author(s):  
Reza Moradi Rad ◽  
Abdolrahman Attar ◽  
Asadollah Shahbahrami
Keyword(s):  
Data Compression ◽  
Multimedia Data ◽  
Predictive Algorithm

scholarly journals Preprocessing Techniques for High-Efficiency Data Compression in Wireless Multimedia Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Junho Park ◽  
Jaesoo Yoo
Keyword(s):  
Sensor Networks ◽  
Data Compression ◽  
High Efficiency ◽  
Wireless Multimedia Sensor Networks ◽  
Multimedia Data ◽  
Wireless Multimedia ◽  
Compression Scheme ◽  
Multimedia Sensor Networks ◽  
Second Stage ◽  
Efficiency Data

We have proposed preprocessing techniques for high-efficiency data compression in wireless multimedia sensor networks. To do this, we analyzed the characteristics of multimedia data under the environment of wireless multimedia sensor networks. The proposed preprocessing techniques consider the characteristics of sensed multimedia data to perform the first stage preprocessing by deleting the low priority bits that do not affect the image quality. The second stage preprocessing is also performed for the undeleted high priority bits. By performing these two-stage preprocessing techniques, it is possible to reduce the multimedia data size in large. To show the superiority of our techniques, we simulated the existing multimedia data compression scheme with/without our preprocessing techniques. Our experimental results show that our proposed techniques increase compression ratio while reducing compression operations compared to the existing compression scheme without preprocessing techniques.

Data Compression Techniques and Standards

2011 ◽  
pp. 61-86
Author(s):  
Phillip K.C. Tse
Keyword(s):  
Image Compression ◽  
Data Compression ◽  
Video Compression ◽  
Storage System ◽  
Multimedia Data ◽  
Jpeg2000 Compression ◽  
Compression Model ◽  
Textual Data ◽  
Mpeg2 Video

In the previous chapter, we see that the performance of a storage system depends on the amount of data being retrieved. The size of multimedia objects are however very large in size. Thus, the performance of the storage system can be enhanced if the object sizes are reduced. Therefore, multimedia objects are always compressed when they are stored. In addition, the performance of most subsystems depends on the amount of data being processed. Since multimedia objects are large in size, their accessing times are long. Thus, multimedia objects are always kept in their compressed form when they are being stored, retrieved, and processed. We shall describe the commonly used compression techniques and compression standards in this chapter. We first describe the general compression model in the next section. Then, we explain the techniques in compressing textual data. This is followed by the image compression techniques. In particular, we shall explain the JPEG2000 compression with details. Lastly, we explain the MPEG2 video compression standard. These compression techniques are helpful to understand the multimedia data being stored and retrieved.

scholarly journals Distributed video coding scheme of multimedia data compression algorithm for wireless sensor networks

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Ning Ma
Keyword(s):  
Sensor Networks ◽  
Video Coding ◽  
Data Compression ◽  
Distributed Video Coding ◽  
Video Codec ◽  
Multimedia Data ◽  
High Definition ◽  
Process Data ◽  
Coding Scheme ◽  
Network Transmission

AbstractThe emergence of multimedia data has enriched people’s lives and work and has penetrated into education, finance, medical, military, communications, and other industries. The text data takes up a small space, and the network transmission speed is fast. However, due to its richness, the multimedia data makes it occupy an ample space. Some high-definition multimedia information even reaches the GB level, and the multimedia data network transmission is relatively slow. Compared with the traditional scalar data, the multimedia data better describes the characteristics of the transaction, but at the same time, the multimedia data itself has a large capacity and must be compressed. Nodes of wireless multimedia sensor networks have limited ability to process data. Traditional data compression schemes require high processing power of nodes and are not suitable for sensor networks. Therefore, distributed video codec scheme in recent years becomes one of the hot multimedia sensor network technologies, which is a simple coding scheme, coding complexity of decoding performance. In this paper, distributed video codec and its associated knowledge based on the study present a distributed video coding scheme and its improvements. Aiming at the problem that the traditional distributed video coding scheme cannot accurately decode the motion severe region and the boundary region, a distributed video coding algorithm based on gradient-domain ROI is proposed, which can enhance the coding efficiency of the severe motion region and improve the decoded image while reducing the code rate and quality, ultimately reducing sensor node energy consumption.

scholarly journals Image Compression Techniques Using Linear Algebra with SVD Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 22-28
Author(s):  
S. Karthigai Selvam ◽  
S. Selvam
Keyword(s):  
Image Compression ◽  
Data Compression ◽  
Linear Algebra ◽  
Storage Capacity ◽  
Multimedia Data ◽  
Experimental Result ◽  
Power Method ◽  
Compression Method ◽  
Huge Amount ◽  
Transmission Bandwidth

In recent days, the data are transformed in the form of multimedia data such as images, graphics, audio and video. Multimedia data require a huge amount of storage capacity and transmission bandwidth. Consequently, data compression is used for reducing the data redundancy and serves more storage of data. In this paper, addresses the problem (demerits) of the lossy compression of images. This proposed method is deals on SVD Power Method that overcomes the demerits of Python SVD function. In our experimental result shows superiority of proposed compression method over those of Python SVD function and some various compression techniques. In addition, the proposed method also provides different degrees of error flexibility, which give minimum of execution of time and a better image compression.

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