Improved H.264-based video coding using an adaptive transform
ABSTRACT In block-based video coding, the Discrete Cosine Transform (DCT) has been adopted for signal decorrelation in state-of-the-art standards. Although the Karhunen Loeve Transform (KLT) is known to achieve optimal energy compaction, it has been reported to offer only moderate compression as the KLT basis functions are source dependent and hence require the transform itself to be coded. This paper describes a technique for prediction-error block coding using the KLT. The proposed method does not require coding of the KLT bases. Instead the basis functions can be derived at the decoder in a manner similar to the encoder. The proposed method is incorporated into a standard H.264 video codec using an adaptive transform selection approach. Our experiments show that the Peak Signal-to-Noise Ratio (PSNR) improvement of up to 0.9 dB is achieved with the proposed technique when compared with the standard H.264 codec.
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ABSTRACT: Discusses various aspects of transform coding, including: source coding, constrained source coding, the standard theoretical model for transform coding, entropy codes, Huffman codes, quantizers, uniform quantization, bit allocation, optimal transforms, transforms visualization, partition cell shapes, autoregressive sources, transform optimization, synthesis transform optimization, orthogonality and independence, and departures form the standard modelIEEE Signal Processing Magazine 10/2001; · 3.37 Impact Factor
Conference Proceeding: Transform coding using adaptive bases and quantization[show abstract] [hide abstract]
ABSTRACT: This paper considers the problem of universal transform coding based on estimating the Karhunen-Loeve transform from quantized data. The use of quantized data in the estimation allows the encoder and decoder to maintain the same state without any side information. A theorem is presented that proves, under certain conditions, that consistent estimation of all the required moments is possible from uniformly scalar quantized data regardless of the quantization coarseness. This consistent estimation requires the solution of nonlinear equations. Very simple approximations that avoid these nonlinear equations are used to develop a practical adaptive coding technique. Promising experimental results obtained with this method are presentedImage Processing, 1996. Proceedings., International Conference on; 10/1996
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ABSTRACT: The combination of singular value decomposition (SVD) and vector quantization (VQ) is proposed as a compression technique to achieve low bit rate and high quality image coding. Given a codebook consisting of singular vectors, two algorithms, which find the best-fit candidates without involving the complicated SVD computation, are described. Simulation results show that the proposed methods are better than the discrete cosine transform (DCT) in terms of energy compaction, data rate, image quality, and decoding complexityIEEE Transactions on Image Processing 09/1995; · 3.20 Impact Factor