Books/Book Chapters/Edited Books

Permanent URI for this collectionhttp://192.168.24.11:4000/handle/123456789/237

Browse

Filters

2022 - 20232

Settings

Author: search.filters.author.Chandra, HarshitHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT)
    (IEEE, 2022) Chandra, Harshit; Bajpai, Shrish
    Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.
  • Thumbnail Image
    Item
    2023 International Conference on Power, Instrumentation, Energy and Control (PIECON)
    (IEEE, 2023) Chandra, Harshit; Bajpai, Shrish
    Computational complexity with the coding efficiency of any hyperspectral image sensor is a challenging issue. 3D-SPIHT has reasonable complexity and generates an embedded bit-stream. Due to the linked lists, the processing of the compression through the 3D-SPIHT gets slow at the high bit rates. This manuscript presents a low-complexity version of 3D-SPIHT which uses the array structure instead of linked lists. These arrays are independent of each other. The coding memory required by the arrays is the same as the linked list used in 3D-SPIHT. Through the use of array, the coding complexity is reduced and coding efficiency is increased with the use of parity of the bit plane. Thus, the proposed compression scheme is an optimum solution for the resource-constraint hyperspectral image sensor.