Channel-Wise Bit Allocation for Deep Visual Feature Quantization

Abstract

Intermediate deep visual feature compression and transmission is an emerging research topic, which enables a good balance among computing load, bandwidth usage and generalization ability for AI-based visual analysis in edge-cloud collaboration. Quantization and the corresponding ratedistortion optimization are the key techniques in deep feature compression. In this paper, by exploring the feature statistics and a greedy iterative algorithm, we propose a channel-wise bit allocation method for deep feature quantization optimizing for network output error. Given the limited rate and computational power, the proposed method can quantize features with small information loss. Moreover, the method also provides the option to handle the trade-offs between computational cost and quantization performance. Experimental results on ResNet and VGGNet features demonstrate the effectiveness of the proposed bit allocation method.

Deep Feature Compression with Collaborative Coding of Image Texture

“…In comparison, the channel with lower sensitivity would be allocated with fewer bits to guarantee that the overall bits do not exceed the given bit cost. In addition, Wang et al [22] investigated the quantization of deep features. Unlike [2,5,9,23,24], which use uniform or logarithmic quantizers to reduce the data volume, it assigns different quantization intervals to each feature channel to achieve minimal network output errors.…”