We present in this paper a novel distributed image coding scheme by exploiting image spatial correlation via Markov random field modeling at the decoding end. This allows us to design a simple yet efficient encoder suitable for various energy efficient imaging sensor network applications. The novelty is the integration of LDPC decoding and Markov random field modeling in order to jointly exploit both interimage and intra-image correlation. The current research aims at improving our previous work in which the Markov model was defined by a state transition probability matrix. In this research, we model the image via a Markov random field described by Gibbs distribution. Both analysis and simulations have been carried out to demonstrate that this Markov model-based approach is able to achieve significant gains over the schemes without Markov modeling. Furthermore, this new Gibbs-based Markov model is less sensitive to correlated noise. Our approach also outperforms a JPEG codec by up to 4 dB even if the interimage correlation is not very high.

Error concealment techniques are very important for video communication since compressed video sequences may be corrupted or lost when transmitted over error-prone networks. In this paper, we propose a novel two-stage error concealment scheme for erroneously received video sequences. In the first stage, we propose a novel spatio-temporal boundary matching algorithm (STBMA) to reconstruct the lost motion vectors (MV). A well defined cost function is introduced which exploits both spatial and temporal smoothness properties of video signals. By minimizing the cost function, the MV of each lost macroblock (MB) is recovered and the corresponding reference MB in the reference frame is obtained using this MV. In the second stage, instead of directly copying the reference MB as the final recovered pixel values, we use a novel partial differential equation (PDE) based algorithm to refine the reconstruction.We minimize, in a weighted manner, the difference between the gradient field of the reconstructed MB in current frame and that of the reference MB in the reference frame under given boundary condition. A weighting factor is used to control the regulation level according to the local blockiness degree. With this algorithm, the annoying blocking artifacts are effectively reduced while the structures of the referenceMBare well preserved. Compared with the error concealment feature implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.

This paper presents a comparison between the scalable extension of H.264/AVC (SVC) and transcoding, in terms of Rate-Distortion(RD) performance and computational complexity. Both bitrate and spatial resolution reduction cases are included. Simulation results show that, in the aspect of RD performance, SVC performs significantly better in bitrate reduction and slightly better in spatial resolution reduction. Meanwhile, SVC adaptation through bitstream extraction has negligible complexity compared with transcoding1.

This paper proposes a video coding scheme, in which textural and structural regions are selectively removed in the encoder, and restored in the decoder by spatio-temporal texture ynthesis and edge-based inpainting. In the proposed scheme, two types of regions are classified based on two motion models: local motion and global motion. In local motion regions, conventional blockbased motion estimation is employed for region removal and spatio-temporal texture synthesis is applied for recovery of the removed regions. In global motion regions, edge-based image inpainting is utilized to recover removed regions, and sprite generation is used as an auxiliary tool to keep temporal consistency. In the proposed scheme, both structures and textures are handled and some kinds of assistant information which can guide restoration are extracted and coded. This approach is blockbased and thus is flexible and generic to be implemented into standard-compliant video coding schemes. It has been implemented into H.264/AVC and achieves up to 35% bitrate saving at similar visual quality levels compared with H.264/AVC without our approach.

With the growing popularity of personal digital assistant devices and smart phones, more and more consumers are becoming quite enthusiastic to appreciate videos via mobile devices. However, limited display size of the mobile devices has been imposing significant barriers for users to enjoy browsing high-resolution videos. In this paper, we present an attention-information-based spatial adaptation framework to address this problem. The whole framework includes two major parts: video content generation and video adaptation system. During video compression, the attention information in video sequences will be detected using an attention model and embedded into bitstreams with proposed supplement-enhanced information (SEI) structure. Furthermore, we also develop an innovative scheme to adaptively adjust quantization parameters in order to simultaneously improve the quality of overall encoding and the quality of transcoding the attention areas.When the high-resolution bitstream is transmitted to mobile users, a fast transcoding algorithm we developed earlier will be applied to generate a new bitstream for attention areas in frames. The new low-resolution bitstream containing mostly attention information, instead of the high-resolution one, will be sent to users for display on the mobile devices. Experimental results show that the proposed spatial adaptation scheme is able to improve both subjective and objective video qualities.