Hierarchical HDTV/SDTV compatible coding using Kalman statistical filtering

• Published in: IEEE transactions on circuits and systems for video technology Vol. 9; no. 3; pp. 424 - 437
• Main Authors: Tihao Chiang, Anastassiou, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.1999; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Coding; Compatibility; Digital TV; Discrete cosine transforms; Exact sciences and technology; Filtering; Filtration; Frequency domain analysis; HDTV; High definition television; Image processing; Information, signal and communications theory; Kalman filters; Mathematical models; Matrix decomposition; Redundancy; Signal analysis; Signal processing; Signal to noise ratio; Telecommunications and information theory; Television; Image quality; Gaussian noise; Image processing; High definition television; Coding circuit; White noise; Kalman filtering; Autoregressive model; Motion compensation; Hierarchical system; Cosine transform; Recursive filtering
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/76.754772

This paper addresses the issue of hierarchical coding of digital television. A two-layer coding scheme is presented to provide compatibility of standard-definition television (SDTV) and high-definition television (HDTV). The scheme is based on a spatio-temporal pyramid coding technique. We address the problem of interlaced-to-interlaced two-layer compatible coding where both layers are interlaced. The resolution translation is important for the visual quality of the SDTV layer and for the performance of the HDTV layer. A motion-compensated up/down deinterlacing scheme is used to improve the performance. A spatio-temporal averaging technique is used to give a better compatible prediction so that the HDTV layer has a high compression performance. To offer an improved prediction, systematic analysis of the remaining statistical redundancy of the enhancement signal is conducted. Based on an autoregressive model of the difference signal, a Kalman statistical filtering is used to exploit such a redundancy. We combine a recursive filtering and discrete cosine transform (DCT) coding using QR decomposition, where Q is an orthonormal matrix and R is an upper triangular matrix. The error accumulation is cancelled in the DCT frequency domain. The results show peak signal-to-noise-ratio improvements over simulcast as high as 1.2 dB. The new technique, which is referred to as spatial scalability using a Kalman filter (SSKF), achieves a comparable or better picture quality than that of a nonscalable approach for high-quality video coding. The near optimal performance is demonstrated by the white Gaussian noise property of the residual signal.