Huynh Van Luong

Side Information and Noise Learning for Distributed Video Coding Using Optical Flow and Clustering

Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjøntegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

Multi-hypothesis transform domain Wyner-Ziv video coding including optical flow

Transform Domain Wyner-Ziv (TDWZ) video coding is an efficient Distributed Video coding solution providing new features such as low complexity encoding, by mainly exploiting the source statistics at the decoder based on the availability of decoder side information. The accuracy of the decoder side information has a major impact on the performance of TDWZ. In this paper, a novel multi-hypothesis based TDWZ video coding is presented to exploit the redundancy between multiple side information and the source information. The decoder used optical flow for side information calculation. Compared with the best available single estimation mode TDWZ, the proposed multi-hypothesis based TDWZ achieves robustly better Rate-Distortion (RD) performance and the overall improvement is up to 0.6 dB at high bitrate and up to 2 dB compared with the DISCOVER TDWZ video codec.

Re-estimation of Motion and Reconstruction for Distributed Video Coding

Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.

Adaptive mode decision with residual motion compensation for distributed video coding

Distributed video coding (DVC) is a coding paradigm that entails low complexity encoding by exploiting the source statistics at the decoder. To improve the DVC coding efficiency, this paper proposes a novel adaptive technique for mode decision to control and take advantage of skip mode and intra mode in DVC. The adaptive mode decision is not only based on quality of key frames but also the rate of Wyner-Ziv (WZ) frames. To improve noise distribution estimation for a more accurate mode decision, a residual motion compensation is proposed to estimate a current noise residue based on a previously decoded frame. The experimental results show that the proposed adaptive mode decision DVC significantly improves the rate distortion performance without increasing the encoding complexity. For a GOP size of 2 on the set of test sequences, the average bitrate saving of the proposed codec is 35.5% on WZ frames compared with the DISCOVER codec.

RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration

Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper, a robust feature point matching algorithm, which is called recovery and filtering vertex trichotomy matching, is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine-invariant descriptor, which is called the vertex trichotomy descriptor, is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier removals in vertex trichotomy matching (VTM) are implemented by iteratively comparing the disparity of the corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large number of outliers are removed in VTM iterations, and several residual outliers that are close to the correct locations cannot be excluded with the same graph structures. Therefore, a recovery and filtering strategy is designed to recover some inliers based on identical vertex trichotomy descriptors and restricted transformation errors. Assisted with the additional recovered inliers, residual outliers can be also filtered out during the process of reaching identical graphs for the expanded vertex sets. Experimental results demonstrate the superior performance on precision and stability of this algorithm under various conditions, such as remote sensing images with large transformations, duplicated patterns, or inconsistent spectral content.

Sparse signal reconstruction with multiple side information using adaptive weights for multiview sources

This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted n-ℓ1 minimization. The proposed algorithm computes the adaptive weights in two levels, first each individual intra-SI and then inter-SI weights are iteratively updated at every reconstructed iteration. This two-level optimization leads the proposed reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to robustly exploit the multiple SIs with different qualities. We experimentally perform our algorithm on generated sparse signals and also correlated feature histograms as multiview sparse sources from a multiview image database. The results show that RAMSIA significantly outperforms both classical CS and CS with single SI, and RAMSIA with higher number of SIs gained more than the one with smaller number of SIs.

A Reconstruction Algorithm with Multiple Side Information for Distributed Compression of Sparse Sources

We consider the task of reconstructing target signals which are processed as sparse sources for a distributed compression scenario, where communication between the sources is prohibited, however, correlation of information among sources can be utilized at the decoder. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI) for such distributed sparse sources. The proposed algorithm takes advantage of both a compressive sensing (CS) reconstruction with SI and an iteratively weighted ℓ1-norm minimization by solving a general weighted multi-ℓ1 (or n-ℓ1) minimization. To utilize the known multiple SIs, the algorithm computes optimal weights on not only each individual SI but among SIs where the weights are adaptively updated according to changes at every iteration of the reconstruction. By this optimization, the proposed reconstruction algorithm with multiple SI (RAMSI) can robustly exploit the multiple SIs with different qualities. We experimentally demonstrate our algorithm on compressing feature histograms as sparse sources which are extracted from a multi-view image database for multi-view recognition. The results show that the RAMSI with multiple SIs efficiently outperforms the ℓ1 minimization and also the CS reconstruction with only one SI.

Parallel iterative decoding of Transform Domain Wyner-Ziv video using cross bitplane correlation

In recent years, Transform Domain Wyner-Ziv (TDWZ) video coding has been proposed as an efficient Distributed Video Coding (DVC) solution, which fully or partly exploits the source statistics at the decoder to reduce the computational burden at the encoder. In this paper, a parallel iterative LDPC decoding scheme is proposed to improve the coding efficiency of TDWZ video codecs. The proposed parallel iterative LDPC decoding scheme is able to utilize cross bitplane correlation during decoding, by iteratively refining the soft-input, updating a modeled noise distribution and thereafter enhancing the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate of Wyner-Ziv frames up to 5.6% and improves the rate-distortion (RD) performance of TDWZ.

Multiple LDPC decoding using bitplane correlation for Transform Domain Wyner-Ziv video coding

Distributed video coding (DVC) is an emerging video coding paradigm for systems which fully or partly exploit the source statistics at the decoder to reduce the computational burden at the encoder. This paper considers a Low Density Parity Check (LDPC) based Transform Domain Wyner-Ziv (TDWZ) video codec. To improve the LDPC coding performance in the context of TDWZ, this paper proposes a Wyner-Ziv video codec using bitplane correlation through multiple parallel LDPC decoding. The proposed scheme utilizes inter bitplane correlation to enhance the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate up to 3.9% and improves the rate-distortion (RD) performance of TDWZ.

Noise residual learning for noise modeling in distributed video coding

Distributed video coding (DVC) is a coding paradigm which exploits the source statistics at the decoder side to reduce the complexity at the encoder. The noise model is one of the inherently difficult challenges in DVC. This paper considers Transform Domain Wyner-Ziv (TDWZ) coding and proposes noise residual learning techniques that take residues from previously decoded frames into account to estimate the decoding residue more precisely. Moreover, the techniques calculate a number of candidate noise residual distributions within a frame to adaptively optimize the soft side information during decoding. A residual refinement step is also introduced to take advantage of correlation of DCT coefficients. Experimental results show that the proposed techniques robustly improve the coding efficiency of TDWZ DVC and for GOP=2 bit-rate savings up to 35% on WZ frames are achieved compared with DISCOVER.