Title: Pseudo sequence based deep neural network compression. Abstract: Along with the performance increase of the neural network, both the number of layers and the number of parameters in each layer are becoming larger and larger. Therefore, there are more and more works trying to compress the neural network efficiently while keeping the performance. However, all of them have not taken the similarity among the kernels into consideration. In this paper, we try to organize the kernels in different channels into a frame and encode the frames using block-based video coding methods. First, we try to reshape the weights in different channels into a pseudo sequence. Second, after obtaining all the frames in the videos, we will convert the weight into the PCA domain to obtain a more compact representation. Then both the intra prediction and the inter prediction will be performed in the PCA domain to achieve better performance. Finally, the uniform quantization and entropy coding will be used to encode the residue blocks. The experimental results show that we can achieve $58$ times compression for the classical VGG-16 model. Not only with very high compression ratio, the proposed method can also provide the benefits of getting a better balance between the bits per weight and the error in more precise granularity by adjusting the quantization parameters.