Joint optimization of scale factors and Huffman code books for MPEG-4 AAC

• Published in: IEEE transactions on signal processing Vol. 54; no. 1; pp. 177 - 189
• Main Authors: Bauer, C., Vinton, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Audio coding; Coding, codes; Constraint optimization; Distortion; Dynamic programming; Encoders; Encoding; Entropy; Exact sciences and technology; Information, signal and communications theory; Iterative methods; Lagrangian functions; Limits; Mathematical model; Mathematical models; Methods; MPEG 4 Standard; Multipliers; Optimization; performance analysis; Searching; Signal and communications theory; signal processing for communications; Studies; Telecommunications and information theory; Viterbi algorithm; Lagrange multiplier; Audio signal processing; Limits; Coding; Huffman code; Performance analysis; Scale factor; signal processing for communications; Optimization
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2005.861090

This paper addresses the optimization problem of minimizing the distortion subject to a rate constraint for an MPEG-4 Advanced Audio Coding (AAC) encoder. We first develop a mathematical model of the AAC encoding process. In previous work, the joint optimization problem is modeled as a Viterbi search for a cheapest path through a trellis. This method involves an iteration over a Lagrangian multiplier. We improve on this method by deriving a very accurate guess for the value of the final Lagrangian multiplier of the iteration as a function of the Perceptual Entropy of the signal and the given rate constraint. This reduces the complexity of the Trellis Search significantly. Whereas previous methods including the Trellis Search did not provide optimal solutions to the problem of minimizing the distortion subject to a rate constraint, we establish two methods that for the first time solve this problem optimally. Our first method is based on the formulation and solution of a Mixed Integer Linear Program, whereas our second method uses a Dynamic Programming solution that does not rely on the iteration over a Lagrangian multiplier. Based on our optimal methods, we evaluate the performance of the heuristic Two Loop Search (TLS), which is used in most commercial AAC implementations to solve the problem under consideration, and the performance of the Trellis Search.