This paper presents a very low-memory video compression architecture for implementation in a wireless multimedia sensor network. The approach employs a strip-based processing technique where a group of image sequences...
详细信息
This paper presents a very low-memory video compression architecture for implementation in a wireless multimedia sensor network. The approach employs a strip-based processing technique where a group of image sequences is partitioned into strips, and each strip is encoded separately. A new one-dimensional, memory-addressing method is proposed to store the wavelet coefficients at predetermined locations in the strip buffer for ease of coding. To further reduce the memory requirements, the video-coding scheme uses a modified set-partitioning in hierarchical trees algorithm to give a high compression performance. The proposed work is implemented using a soft-core microprocessor-based approach. Simulation tests conducted have verified that even though the proposed video compression architecture using strip-based processing requires a much less complex hardware implementation and its efficient memory organisation uses a lesser amount of embedded memory for processing and buffering, it can still achieve a very good compression performance.
For efficient video compression, the 3 Dimensional set-partitioning in hierarchical trees (3D-SPIHT) algorithm and 3D wavelet transform are used. The 3D wavelet transform is based on the group of frames concept. For d...
详细信息
For efficient video compression, the 3 Dimensional set-partitioning in hierarchical trees (3D-SPIHT) algorithm and 3D wavelet transform are used. The 3D wavelet transform is based on the group of frames concept. For decomposition, wavelet filters are used. The transform coefficients are coded by 3D SPIHT. In the reconstruction phase, 3D SPIHT decoding algorithm and inverse transforms are employed. A modified SPIHT algorithm called Block Based Pass Parallel SPIHT (BPSPIHT) is employed for video compression to overcome the slow processing speed of SPIHT. The video is detached into individual frames. Then Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) are employed on that frames. The wavelet coefficients are encoded using BPSIHT encoder and the generated bit streams are decoded by BPSPIHT decoder. For the reconstruction, inverse wavelet transform is applied and the calibre is measured in terms of PSNR, MSE and Compression Ratio (CR).
暂无评论