Online dictionary learning based intra-frame video coding

In this paper, we propose 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 with convergence guaranteed, leading 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. Then, these non-zero coefficients for image patches are further quantized and coded combined with dictionary synchronization. The experimental results demonstrate that the number of non-zero coefficients of each frame decreases rapidly while more frames are encoded. Compared to the off-line mode training, the proposed ODL codec, learning from video on the fly, is able to reduce the computational complexity with fast convergence. Finally, 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.