Low Bitrate Image Coding via Interpolation Oriented Adaptive Downsampling An interpolation oriented adaptive downsampling algorithm is proposed for low bitrate image coding in this paper. Given an image, the proposed algorithm is able to obtain a low resolution image, from which a high quality image with the same resolution as the input image can be interpolated. Different from the traditional downsampling algorithms, which are independent from the interpolation process, the proposed downsampling algorithm hinges the downsampling to the interpolation process. Consequently, the proposed downsampling algorithm is able to maintain the original information of the input image to the largest extent. The downsampled image is then fed into JPEG. A total variation (TV) based post processing is then applied to the decompressed low resolution image. Ultimately, the processed image is interpolated to maintain the original resolution of the input image. Experimental results verify that utilizing the downsampled image by the proposed algorithm, an interpolated image with much higher quality can be achieved. Besides, the proposed algorithm is able to achieve superior performance than JPEG for low bit rate image coding.

Adaptive regularized image interpolation using data fusion and steerable constraints This paper presents an adaptive regularized image interpolation algorithm from blurred and noisy low resolution image sequence, which is developed in a general framework based on data fusion. This framework can preserve the high frequency components along the edge orientation in a restored high resolution image frame. This multiframe image interpolation algorithm is composed of two levels of fusion algorithm. One is to obtain enhanced low resolution images as an input data of the adaptive regularized image interpolation based on data fusion. The other one is to construct the adaptive fusion algorithm based on regularized image interpolation using steerable orientation analysis. In order to apply the regularization approach to the interpolation procedure, we first present an observation model of low resolution video formation system. Based on the observation model, we can have an interpolated image which minimizes both residual between the high resolution and the interpolated images with a prior constraints. In addition, by combining spatially adaptive constraints, directional high frequency components are preserved with efficiently suppressed noise. In the experimental results, interpolated images using the conventional algorithms are shown to compare the conventional algorithms with the proposed adaptive fusion based algorithm. Experimental results show that the proposed algorithm has the advantage of preserving directional high frequency components and suppressing undesirable artifacts such as noise.