Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model
This work addresses scalable image acquisition and encoding for applications like multimedia transmission, but it is incremental as it builds on existing compressed sensing techniques.
The paper tackles the problem of scalable compressed image sensing by proposing a method that generates two bit-streams for different quality levels, using inter-layer prediction to encode residuals. The result is improved reconstruction quality with modest complexity, achieving gains over both separate encoding and non-scalable systems.
Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In this paper we propose a novel method to provide a scalable encoding of an image acquired by means of compressed sensing techniques. Two bit-streams are generated to provide two distinct quality levels: a low-resolution base layer and full-resolution enhancement layer. In the proposed method we exploit a fast preview of the image at the encoder in order to perform inter-layer prediction and encode the prediction residuals only. The proposed method successfully provides resolution and quality scalability with modest complexity and it provides gains in the quality of the reconstructed images with respect to separate encoding of the quality layers. Remarkably, we also show that the scheme can also provide significant gains with respect to a direct, non-scalable system, thus accomplishing two features at once: scalability and improved reconstruction performance.