KEYWORDS: Quantization, Matrices, Video coding, Computer programming, Video, Spatial frequencies, Visual system, Video compression, Decision support systems, Control systems
Quantization matrix is an important encoding tool for discrete cosine transform (DCT) based perceptual image / video
encoding in that DCT coefficients can be quantized according to the sensitivity of the human visual system to the
coefficients' corresponding spatial frequencies. A quadratic model is introduced to parameterize the quantization
matrices. This model is then used to optimize quantization matrices for a specific bitrate or bitrate range by maximizing
the expected encoding quality via a trial based multidimensional numerical search method. The model is simple yet it
characterizes the slope and the convexity of the quantization matrices along the horizontal, the vertical and the diagonal
directions. The advantage of the model for improving perceptual video encoding quality is demonstrated with
simulations using H.264 / AVC video encoding.
We introduce a systematic approach to configuring the video encoding parameters for optimal video encoding in this
paper. The determination of optimal video encoding parameters is formulated as an optimization problem of maximizing
the expected video encoding quality under a set of constraints that may include a video quality measure, a target bitrate,
computation, memory bandwidth, etc. We use the Video Quality Metric, a measurement paradigm of video quality that
is based on algorithms for objective measurement of video quality, to measure the expected video encoding
performance. The optimization problem can be solved through an efficient multidimensional numerical search method,
direct simplex search method, with encoding of various sequences with different encoding parameter settings. We
illustrate the approach to determine parameters to enable optimal MB level quantization parameter adaptation in H.264 /
AVC.
The paper studies scalable video coding based on multiresolution
video representations generated by multi-scale subband motion
compensated temporal filtering (MCTF) and spatial wavelet
transform. Since MCTF is performed subband by subband in the
spatial wavelet domain, motion vectors are available for
reconstructing video sequences of any possible reduced spatial
resolution, restricted by the dyadic decomposition pattern and the
maximal spatial decomposition level. The multiresolution
representations naturally provide a framework with which both
spatial scalability and temporal scalability can be very
conveniently and efficiently supported by a video coder that
utilizes such multiresolution video representations. Such video
coders can be fully scalable by incorporating wavelet-domain
bit-plane image coding techniques. This paper examines the
performance, including scalability and coding efficiency, of a
scalable video coder that utilizes such multi-scale video
representations together with the EZBC image coder. A
wavelet-domain variable block size motion estimation algorithm is
introduced to enhance the performance of the subband MCTF.
Experiments show that the proposed coder outperforms the state of
the art fully scalable coder MC-EZBC in terms of the spatial
scalability.
The purpose of this paper is to study signal denoising by thresholding coefficients of undecimated discrete wavelet packet transforms (UDWPT). The undecimated filterbank implementation of UDWPT is first considered, and the best basis selection algorithm that prunes the complete undecimated discrete wavelet packet binary tree is studied for the purpose of signal denoising. Distinct from the usual approach which selects the best subtree based on the original (unthresholded) transform coefficients, our selection is based on the thresholded coefficients, since we believe discarding the small coefficients permits to choose the best basis from the set of coefficients that will really contribute to the reconstructed signal. Another feature of the algorithm is the thresholding scheme. To threshold coefficients which are correlated differently from scale to scale and from band to band, a uniform threshold is not appropriate. Alternatively, two scale-band-dependent thresholding schemes are designed: a correlation-dependent model and a Monte Carlo simulation-based model. The cost function for the pruning algorithm is specifically designed for the purpose of signal denoising. We consider it profitable to split a band if more noise can be discarded by thresholding while signal components are preserved. So, higher SNR is desirable in the process of selection. Experiments conducted for 1D and 2D signals shows that the algorithm achieves good SNR performance while preserving high frequency details of signals.
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