Joint optimization of JPEG quantization table and coefficient thresholding for low bitrate mobile visual search
Low latency query delivery over wireless network is a key problem for mobile visual search. Extracting compact descriptors directly on the mobile device is computational expensive, an alternate approach is to send highly compressed JPEG query images. As JPEG baseline optimizes the rate-distortion from a perceptual perspective rather than maintaining search performance, recent work proposed to learn a feature-preserving JPEG quantization table for improved search accuracy. However, this method is data-dependent and the quantization table cannot adapt to image blocks. To address these issues, we propose to jointly optimize the JPEG quantization table and coefficient thresholding. The matching score between uncompressed image and its compressed JPEG image is employed as the distortion measure to avoid time consuming image labeling, and coefficient thresholding eliminates the redundant coefficients. Extensive experiments on benchmark datasets show that our approach obtains superior performance than state-of-the-art at low bitrates, meanwhile, it consumes lower cost including processing time, memory and battery on mobile device.