Compressed sensing based quantization with prediction encoding for video transmission in WSN

Wireless multimedia sensor networks have limited computational resources such as bandwidth, storage and energy. To overcome these limitations, a promising technique called compressed sensing (CS) is adopted to develop the video encoding algorithm. CS is the process of acquiring and reconstructing a signal which is sparse thus reducing the computational complexity. The source video frames are converted into sparse components by applying sparsifying transform. The measurements obtained from the sparse components using CS are quantized and encoded by proposed algorithm for efficient storage and transmission. To represent the data with reduced number of bits an efficient compressed sensing based quantized prediction Huffman encoder is presented in this paper. The orthogonal matching pursuit recovery algorithm is used at the reconstruction side to get back the original sparse components. The performance of the video encoder is evaluated using compression ratio in terms of percentage. The PSNR and SSIM of the recovered frames show promising results thus prove to be compatible for wireless multimedia sensor networks.