B.Tech(Electronics and Communication Engineering), MTech (Communication and Information Systems)
Low memory image coders for IoT/visual sensor nodes, Image quality assessment
4/1175J, New Sir Syed Nagar, Aligarh
- A block-based parallel ZM-SPECK algorithm
The Zero Memory Set Partitioned Embedded Block (ZM-SPECK) algorithm is an embedded and memory-efficient image compression algorithm. However, it is computationally complex due to the recursive significance testing of sets and coefficients in each bit plane. To overcome this limitation, it is proposed to parallelize the algorithm over smaller blocks to reduce the overall encoding and decoding times of the ZM-SPECK algorithm. The proposed approach called block-based parallel ZM-SPECK (BPZM-SPECK) decomposes the wavelet transformed image into independent non-overlapping spatial blocks utilizing the unique child-parent relationships in spatial orientation trees (in wavelet domain) and simultaneously encodes all bits in each bit plane of a block. The experimental results show significant improvement in computation time over the existing ZM-SPECK algorithm.
- Modified ZM-SPECK: A Low Complexity and Low Memory Wavelet Image Coder for VS/IoT Nodes
The Zero Memory Set Partitioned Embedded Block (ZM-SPECK) algorithm is a memory-efficient block-based image coder used to encode the wavelet transformed images. It is a listless form of the SPECK algorithm and has no provision for saving the significance information for various coefficients/sets. Since it performs significance testing of sets recursively at each bit-plane, therefore it is computationally complex. Due to this limitation, it is difficult to implement it on low-resource sensor/IoT nodes. In this paper, its low complexity version named Modified Zero Memory Set Partitioned Embedded Block (MZM-SPECK) is proposed which reduces the recursive significance test of sets. The simulation results indicate that the proposed algorithm surpasses other wavelet-based coders with regard to memory and computational complexity while maintaining excellent coding efficiency.