Kyung-Yeon Min

Side information generation using adaptive search range for distributed video coding

In this paper, we propose a new side information generation (SIG) method which is applicable to distributed video coding (DVC). The proposed method adaptively changes a search range depending on a confidence level in hierarchical motion estimation (ME) and reconstructs the hole regions using the proposed uni-directional prediction and linear interpolation. In order to improve reliability of the hierarchical ME, ME for each block is performed in descending order of block variance values. Also, in order to alleviate errors from inaccurate motion vector (MV) estimation, search range is adaptively adjusted according to reliability of ME. Experimental results show that the proposed algorithm is better than several conventional methods for DVC. We also found that block artifacts and blur artifacts are significantly diminished by the proposed algorithm.

Adaptive distributed video coding with motion vectors through a back channel

In this paper, a new adaptive distributed video coding (DVC) method with received motion vectors (MVs) through a back channel is proposed. The MVs estimated for side information (SI) generation in the proposed DVC decoder are transmitted to the proposed DVC encoder to reconstruct a predicted SI (PSI) which is the same as the SI at the decoder with minimum computational load. Therefore, the proposed DVC encoder can determine the positions and magnitudes of errors using the PSI. With available error information, the proposed encoder adaptively determines appropriate slice partitions by maintaining fixed per-slice error rates for prevention of channel decoding failure. The encoder also sends a coded SI accuracy map to the decoder and sets the conditional probability of each variable node of the low-density parity-check accumulator (LDPCA) to the correct crossover probability according to the coded map as received on the decoder side. The performance of the LDPCA can also be significantly improved with a minimum number of parity bits and low computational complexity because accurate belief propagation can be carried out using the correct crossover probabilities in the LDPCA decoder. Experimental results show that the rate-distortion performance of the proposed algorithm is twice that of several conventional DVC methods and that the proposed decoder is approximately 24.5% faster than these methods.

Distributed video coding based on adaptive slice size using received motion vectors

In this paper, we propose a new distributed video coding (DVC) method based on adaptive slice size using received motion vectors (MVs). In the proposed algorithm, the MVs estimated at a DVC decoder are transmitted to a corresponding encoder. In the proposed encoder, a predicted side information (PSI) is reconstructed with the transmitted MVs and key frames. Therefore, the PSI can be generated same to side information (SI) at the decoder. We can, also, calculate an exact crossover probability between the SI and original input frame using PSI and the original frame. As a result, the proposed method can transmit minimum parity bits to maximize error correction ability of a channel decoder with minimal computational complexity. Experimental results show that the proposed algorithm is better than several conventional DVC methods.

Bidirectional Mesh-Based Frame Rate Up-Conversion

The authors propose a new frame rate up-conversion (FRUC) method for temporal video quality enhancement. To cope with translation as well as scale and rotation changes, the algorithm generates an interpolated frame between two given frames on the basis of bidirectional mesh interpolation (BMI). BMI performance is highly influenced by the accuracy of the correspondences between the control points in the two frames. To achieve an accurate, dense motion vector map (MVM) through bidirectional and unidirectional motion estimation, the algorithm forms an initial MVM using motion vectors transmitted from the coded bitstream. Then, the algorithm generates the interpolated frame using frame-based BMI with the dense MVM. In experiments, the authors found that the proposed algorithm is about two decibels better than several conventional FRUC methods. Furthermore, the proposed algorithm significantly diminishes block artifacts and blur artifacts.

Confidence-based adaptive frame rate up-conversion

In this article, we propose a new frame rate up-conversion (FRU) method for temporal quality enhancement. The proposed FRU algorithm employs gradual and adaptive motion estimation based on confidence priority for selecting more accurate motion vectors (MVs). In order to estimate accurate MVs, we adaptively alternate a search range and the order of blocks to be searched depending on the confidence level in hierarchical motion estimation. The precedence of the proposed algorithm is conducted based on the confidence level that is decided by the complexity of pixel values in a block. In addition, we perform bi-directional motion compensation and spatial linear interpolation to fill occlusion regions. In our experiments, we found that the proposed algorithm is about 2 dB better than several conventional methods. Furthermore, block artifacts and blur artifacts are significantly diminished by the proposed algorithm.

LDPC based distributed video coding with multiple side information sets

The side information (SI) used to decode the original frame in the distributed video coding (DVC) is created by a key frame encoded by an existing video codec. This paper proposes a method to restore the original frame using the characteristics of multiple sets of SI and low-density parity-check (LDPC) code. It was identified that the optimal SI could be made by combining multiple sets of SI restored through repeated decoding of SI and LDPC that the SI could be restored to be the closest to the original frame using the optimal SI.

Transcoding from Distributed Video Coding to H.264/AVC Based on Motion Vectors of Side Information

In this paper, a transcoding method with low computational complexity and high coding efficiency is proposed to transcode distributed video coding (DVC) bitstreams to H.264/AVC ones. For the proposed high-performance transcoding with low complexity, not only Wyner-Ziv frames but also key frames can be transcoded with motion vectors estimated in generation of side information. As a motion vector is estimated from a key frame to a prior key frame for side information generation, the motion vector can be used to encode the intra key frame as a predicted frame. Motion estimation is performed with two predicted motion vectors. One is the motion vector from side information generation and the other is median of motion vectors of neighboring blocks. The proposed method selects the best motion vector between two motion vectors based on rate-distortion optimization. Coding efficiency can be improved with a small size of search range, because a motion vector estimated in side information generation is used as an initial motion vector for transcoding. In the experimental results, complexity of transcoder is reduced about 12% and bitrate performance increases about 28.7%.

Complexity Balancing for Distributed Video Coding Based on Entropy Coding

In this paper, a complexity-balancing algorithm is proposed for distributed video coding based on entropy coding. In order to reduce complexity of DVC-based decoders, the proposed method employs an entropy coder instead of channel coders and the complexity-balancing method is designed to improve RD performance with minimal computational complexity. The proposed method performs motion estimation in the decoder side and transmits the estimated motion vectors to the encoder. The proposed encoder can perform more accurate refinement using the transmitted motion vectors from the decoder. During the motion refinement, the optimal predicted motion vectors are decided by the received motion vector and the predicted motion vectors and complexity load of block is allocated by adjusting the search range based on the difference between the received motion vector and the predicted motion vectors. The computational complexity of the proposed encoder is decreased 11.9% compared to the H.264/AVC encoder and that of the proposed decoder are reduced 99% compared to the conventional DVC decoder.