Fixed-Point Inference Of Neural Image Compression

Recent learned image coding has emerged with superior efficiency to conventional methods.It,however,is criticized for its complexity-exhaustive deep neural network(DNN)architectures,especially on resource-constrained mobile platforms.Thus we devise a two-stage approach:first,a range pre-processing is applied to constrain the dynamic range of feature map activation by leveraging its sparsity nature with densely clustered distribution,then a layer-wise range-adaptive quantization for convolutional parameter(e.g.,weight,bias),and simple yet efficient linear scaling and range-dependent normalization for activation are executed,leading to a fully fixed-point inference architecture.All arithmetic operations and associated data tensors are processed using low-bit-width fixed-point numbers,yielding significant reductions of the computational complexity,memory space,and the elimination of platformdependent inconsistency induced by floating-point operations.We first exemplify such fixed-point inference in a DNN-based image decoder,showing the comparable coding efficiency with its native floating-point model,against the same anchor using the High-Efficiency Video Coding(HEVC)-based intra image coder.We also extend proposed approach to the super-resolution network for learned resolution scaling-based video streaming,and VGG network-based classification tasks,both of which present negligible performance loss.These evidence the generalization of our approach for efficient DNN processing of various tasks.All materials are made publicly accessible at http://njuvision.github.io/fixed-point/.