Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically,
sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous
latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their
common sparse latent semantic structure so that the learned visual dictionary is able to describe both the
visual and textual properties of training data. Experiments on both image categorization and retrieval tasks
demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse
The JPEG2000 image coding standard defines two kinds of region of interest (ROI) compression methods--the general
scaling based method and the maximum shift (maxshift) method. There are two major drawbacks of the two methods.
First, they would significantly reduce the compression efficiency by increasing the dynamic range (or number of
bitplanes) of wavelet coefficients. Second, they do not have the especial protection for the ROI against the bit errors in
image communication application, i.e., remote medical treatment and Satellite Communication. When the bit errors
occurred, the reconstruction quality of ROI and BG would reduce at the same time. In this paper, a new and flexible
ROI image compression method is proposed according to the Wyner-Ziv theorem on source coding with side
information. The reconstructed low quality ROI is regard as a noisy version of original ROI in the proposed method. So,
along with the parity bits from the original ROI, the reconstructed low quality ROI as side information can be utilized to
Turbo decoder to decode the high quality ROI. Our experimental results show that the proposed method applying the
Wyner-Ziv coding to ROI compression can highly improve the compression efficiency as well as efficiently protect the
ROI against the bit errors.
Scalable video coding (SVC) has a great advantage due to its very easy adaptation to unpredictable bandwidth variations and network conditions. However, in some scalable video coding schemes, either a single prediction is used, which leads to either drift or coding inefficiency, or a different prediction is obtained for each reconstructed version, which leads to add complexity. So, the new techniques have to be developed to improve the performance of the SVC scheme. Wyner-Ziv coding gives us the surprising insight that efficient data compression can also be achieved by exploiting source statistics-partially or wholly-at the decoder only. Based on the Wyner-Ziv framework, Xu and Xiong proposed a Layer Wyner-Ziv scheme (LWZ) by treating a standard coded video as a base layer, and building the bit-plane enhancement layer using Wyner-Ziv coding. The LWZ scheme has error robustness and channel adaptation. However,
the coding efficiency gap between the nonscalable video coding and LWZ is very large. The reason is that only the current base layer and lower enhancement layers are used as side information, and the correlation between the temporal adjacent frames isn't utilized. In order to improve the coding efficiency of LWZ, a scalable video coding based on Wyner-Ziv (SVC-WZ) is proposed in this paper. In our SVC-WZ scheme, we try to use the correlation between temporal adjacent frames rather than always use the correlation between the enhancement layers and the current base layer as SI during the enhancement layer encoding. Using the correlation between temporal adjacent frames would make SI more accurate, thus it could improve the coding efficiency. Our experimental results show that the SVC-WZ scheme can achieve consistently better coding efficiency than the LWZ scheme while keeping all the properties of LWZ.
In this paper, a novel robust image coder with scalable resolution is presented, called Robust ZeroBlock Wavelet (RZBW), which is suitable for image transport over a noisy channel. In the coder, the zeroblock-based coding algorithm is used, which proved to be an efficient technique for exploiting the clustering of energy found in image transforms. The coder provides both excellent compression performance and graceful degradation over noisy channel. The coder compresses the wavelet coefficients from low frequency to high frequency, so the resolutions ofthe reconsiructed image are scalable.