Deep Neural Network and Weighted Ridge Regression Based Pixel Prediction Technique

Lossless image compression consists of two processes, pixel prediction and residue encoding. Recently, deep neural network (DNN) has been widely adopted in signal prediction. In this work, we apply a DNN architecture together with the information of surrounding pixels weighted by their similarities for pixel prediction. Simulations show that, with the proposed algorithm, the pixels can be predicted more precisely, which is very helpful for lossless image compression.

[1]  Jian-Jiun Ding,et al.  Adaptive Prediction, Context Modeling, and Entropy Coding Methods for CALIC Lossless Image Compression , 2019, 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS).

[2]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[3]  Nasir D. Memon,et al.  CALIC-a context based adaptive lossless image codec , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[4]  G. Blelloch.  Introduction to Data Compression * , 2022 .

[5]  Jarno Mielikäinen,et al.  Clustered DPCM for the lossless compression of hyperspectral images , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  Guillermo Sapiro,et al.  The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS , 2000, IEEE Trans. Image Process..

[7]  Michael T. Orchard,et al.  Edge directed prediction for lossless compression of natural images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).