Quannet: Joint Image Compression and Classification Over Channels with Limited Bandwidth

Lahiru D. Chamain, Sen-ching Samson Cheung, Zhi Ding

Abstract: The performance of cloud based image classification depends critically on its allocated bandwidth. Traditional data compression methods can negatively impact classification accuracy under limited bandwidth. We investigate the design of bandwidth efficient quantization for image encoding and compression with minimum classification accuracy loss. This work develops a simple neural network framework for joint quantization and classification. The proposed 'QuanNet' can optimize the quantization intervals of JPEG2000 encoder to minimize the classification loss. We show that our quantizer optimization can achieve significant accuracy improvement for a given channel bandwidth. Similarly, significant bandwidth can be saved to achieve a desired accuracy for cloud based image classification.