We propose a fast and efficient algorithm which finds the optimal quad-tree (QT) decomposition with leaf dependencies in the rate distortion sense. The underlying problem is the encoding of an image by a variable bloc...
详细信息
ISBN:
(纸本)0819424358
We propose a fast and efficient algorithm which finds the optimal quad-tree (QT) decomposition with leaf dependencies in the rate distortion sense. The underlying problem is the encoding of an image by a variableblocksize scheme, where the blocksize is encoded using a QT, each block is encoded by one of the admissible quantizers and the quantizers are transmitted using a first order differential pulse code modulation (DPCM) scheme along the scanning path. First we define an optimal scanning path for a QT such that successive blocks are always neighboring blocks. Then we propose a procedure which infers such an optimal path from the QT-decomposition and introduce a special optimal path which is based on a Hilbert curve. Then we consider the case where the image is losslessly encoded using a QT structure and propose a dynamic programming (DP) based multi-level approach to find the optimal QT-decomposition and the optimal quantizer selection. We then apply the Lagrangian multiplier method to solve the lossy case, and show that the unconstrained problem of the Lagrangian multiplier method can be solved using the algorithm introduced for the lossless case. Finally we present a mean value QT-decomposition example, where the mean values are DPCM encoded.
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the ...
详细信息
ISBN:
(纸本)9781467399616
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the first and second moments of decoder-reconstructed pixels. An alternative estimation approach, the spectral coefficient wise optimal recursive estimate (SCORE), tracks instead moments of decoder-reconstructed transform coefficients, which enables accounting for transform domain operations. However, the SCORE formulation relies on a fixed transform blocksize, which is incompatible with recent standards. This paper proposes a non-trivial generalization of the SCORE framework which, in particular, accounts for arbitrary blocksize combinations involving the current and reference block partitions. This seemingly intractable objective is achieved by a two-step approach: i) Given the fixed blocksize moments of a reference frame, estimate moments of transform coefficients for the codec-selected current block partition;ii) Convert the current results to transform coefficient moments corresponding to a regular fixed blocksize grid, to facilitate EED estimation for the next frame. Experimental results first demonstrate the accuracy of the proposed estimate in conjunction with transform domain temporal prediction. Then the estimate is leveraged to optimize the coding mode and yields considerable gains in rate-distortion performance.
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the ...
详细信息
ISBN:
(纸本)9781467399623
The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the first and second moments of decoder-reconstructed pixels. An alternative estimation approach, the spectral coefficient-wise optimal recursive estimate (SCORE), tracks instead moments of decoder-reconstructed transform coefficients, which enables accounting for transform domain operations. However, the SCORE formulation relies on a fixed transform blocksize, which is incompatible with recent standards. This paper proposes a non-trivial generalization of the SCORE framework which, in particular, accounts for arbitrary blocksize combinations involving the current and reference block partitions. This seemingly intractable objective is achieved by a two-step approach: i) Given the fixed blocksize moments of a reference frame, estimate moments of transform coefficients for the codec-selected current block partition;ii) Convert the current results to transform coefficient moments corresponding to a regular fixed blocksize grid, to facilitate EED estimation for the next frame. Experimental results first demonstrate the accuracy of the proposed estimate in conjunction with transform domain temporal prediction. Then the estimate is leveraged to optimize the coding mode and yields considerable gains in rate-distortion performance.
暂无评论