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It plays a fundamental role to compactly represent the visual informationtowards the optimization of the ultimate utility in myriad visual data centeredapplications. With numerous approaches proposed to efficiently compress thetexture and visual features serving human visual perception and machineintelligence respectively, much less work has been dedicated to studying theinteractions between them. Here we investigate the integration of feature andtexture compression, and show that a universal and collaborative visualinformation representation can be achieved in a hierarchical way. Inparticular, we study the feature and texture compression in a scalable codingframework, where the base layer serves as the deep learning feature andenhancement layer targets to perfectly reconstruct the texture. Based on thestrong generative capability of deep neural networks, the gap between the basefeature layer and enhancement layer is further filled with the feature leveltexture reconstruction, aiming to further construct texture representation fromfeature. As such, the residuals between the original and reconstructed texturecould be further conveyed in the enhancement layer. To improve the efficiencyof the proposed framework, the base layer neural network is trained in amulti-task manner such that the learned features enjoy both high qualityreconstruction and high accuracy analysis. We further demonstrate the frameworkand optimization strategies in face image compression, and promising codingperformance has been achieved in terms of both rate-fidelity and rate-accuracy.

Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression