Fine-granular scalability (FGS) has been accepted as the streaming profile of MPEG-4 to provide a flexible foundation
for scaling the enhancement layer (EL) to accommodate variable network capacity. To support smooth quality
reconstruction of different rate constraints during transmission, it's significant to acquire the actual rate-distortion
functions (RDF) or curves (RDC) of each frame in MPEG-4 FGS videos. In this paper, firstly, we use zero-mean
generalized Gaussian distributions (GGD) to model the distributions of 64 (8*8) different discrete cosine transform
(DCT) coefficients of FGS EL in a frame. Secondly, we decompose and analyze the FGS coding system using
quantization theory and rate-distortion theory, and then combine the analysis of each component together to form a
complete RDF of the EL. Guided by the above analysis, at last, we introduce a simple and effective rate-distortion (RD)
model to approximate the actual RDF of the EL in MPEG-4 FGS videos. Extensive experimental results show our
statistical model, composition and approximation of actual RDF are efficient and effective. What's more, our analysis
methods are general, and the RDF model can also be used in more widely related R-D areas such as rate control
algorithms.
Feature extraction is very important to pattern recognition. For many image recognition tasks, it is very hard to directly extract the explicit geometrical features of the images. In this case, global feature extraction is often used. Principal Component Analysis (PCA) is a typical global feature extraction method. However, PCA assumes the image population as Gaussian distribution and produces a set of compact features, which are the coefficients of the basis functions with largest eigenvalues. Compared with compact features of PCA, sparse features seem more attractive for recognition tasks. In this paper, three algorithms that produce sparse feature are studied. Independent Component Analysis (ICA) and sparse coding (SP) can describe non-Gaussian distribution. The discriminatory sparse coding (DSP) is a variation of SP, which incorporates class label information of the training samples. Experiments results of face recognition show sparse features have more advantage over compact features. DSP gets the best results for its clustering property of the features.
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