Online dictionary learning based intra-frame video coding via sparse representation

We propose in this paper an online learning based intra-frame video coding approach, exploiting the texture sparsity of natural images. The proposed method is capable of learning the basic texture elements from previous frames that leads to effective dictionaries for sparser representation of incoming frames. Benefiting from online learning, the proposed online dictionary learning based codec (ODL codec) is able to achieve a goal that the more video frames are being coded, the less non-zero coefficients are required to be transmitted. These nonzero coefficients for image patches are further quantized and coded, combined with dictionary synchronization. Experimental results demonstrate that the number of non-zero coefficients of each frame decreases rapidly while more frames are encoded. The rate distortion performance shows improvement in terms of PSNR compared with the K-SVD dictionary based compression and H.264/AVC for intra-frame video at low bit rates.