The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost.
The progress has been made possible, to a large extent, by efficient image/video compression techniques that allow a reduction in the amount of data to be stored and transmitted; therefore, fewer resources were required for this while maintaining image quality.
This paper proposes an efficient bitmask-based compression technique to drastically reduce the control word size while keeping the decompression overhead in an acceptable range.
Many data compression techniques are available for efficient source coding [5, 6].
Moreover, the recent growth of large volume of image data in web applications has not only necessitated the need for more efficient image processing techniques but also for more efficient compression methods for storage and transmission applications[1, 2].
In order to compare and evaluate these multi-stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed.
For an efficient management of lookup tables, they experimented with different compression techniques.
