this article, we utilize the terms SR, HR, and resolution enhancement interchangeably. ) For example, literature reviews are presented in [1] and [2] as well as this special section. Work traditionally addresses the resolution enhancement of frames that are filtered and down-sampled during acquisition and corrupted by additive noise during transmission and storage. In this article, though, we review approaches for the SR of compressed video. Hybrid motion compensation and transform coding methods are the focus, which incorporates the family of ITU and MPEG coding standards [3], [4]. The JPEG still image coding systems are also a special case of the approach

Abstract—Considerable attention has been directed to the problem of producing high-resolution video and still images from multiple low-resolution images. This multiframe reconstruction, also known as super-resolution reconstruction, is beginning to be applied to compressed video. Super-resolution techniques that have been designed for raw (i.e., uncompressed) video may not be effective when applied to compressed video because they do not incorporate the compression process into their models. The compression process introduces quantization error, which is the dominant source of error in some cases. In this paper, we propose a stochastic framework where quantization information as well as other statistical information about additive noise and image prior can be utilized effectively. Index Terms—DCT-domain reconstruction, MAP, multiframe image reconstruction, POCS, super-resolution.

Abstract not found

In this paper, we address the problem of obtaining a high resolution (HR) image from a compressed low resolution (LR) video sequence. Motion information plays a critical role in solving this problem and we determine which pixels in the sequence provide useful information for calculating the high resolution image. The bit-stream of hybrid motion compensated video compression methods includes low resolution motion compensated images; we therefore also study which pixels in these images should be used to increase the quality of the reconstructed image. Once the useful (observable) pixels in the low resolution and motion compensated sequences have been detected, we modify the acquisition model to only account for these observations. The proposed approach is tested on real compressed video sequences and the improved performance is reported.

Super-resolution is the task of estimating high resolution images from a set of low resolution observations. These observations are acquired by either multiple sensors imaging a single scene or by a single sensor imaging the scene over a period of time. No matter the source of observations though, the critical requirement for super-resolution is that the