High Bit-Depth Image Acquisition Framework Using Embedded Quantization Bias

In this paper, we present a novel image acquisition framework capable of reconstructing high bit-depth images using an array of low bit-depth scalar quantizers. Our key contribution is a codesign of pixel quantization and reconstruction in image acquisition pipeline. Different from the traditional image acquisition scheme, where each pixel value is quantized and reconstructed independently, the proposed framework is designed based on the interpixel correlations in local image regions. The interpixel correlations imply that quantized pixel values of adjacent locations can be interpreted as multiple descriptions of a common pixel value. Because combining multiple descriptions leads to reduced uncertainty, a pixel value with higher bit depth can be reconstructed by exploiting the interpixel correlations. In this paper, we propose to inject an embedded quantization bias (EQB) to the prequantized image signal and feed the sum to scalar quantizers. Injecting EQB has the same effect as shifting the quantizers by different amounts at different pixel locations, driving the quantized values of adjacent pixels to form informative descriptions of a common pixel value. We present comprehensive studies on this framework, including the optimal design of the EQB signal, the reconstruction strategies for noisy input, and generalized assumptions on interpixel correlations. We show in the experiments that the proposed image acquisition framework significantly outperforms the competing methods and effectively improves the quality of the reconstructed images.