Recent advances in rate control for video coding
Image Communication
Dynamic computational complexity and bit allocation for optimizing H.264/AVC video compression
Journal of Visual Communication and Image Representation
Designing autonomous layered video coders
Image Communication
Operational distortion-quantization curve-based bit allocation for smooth video quality
Journal of Visual Communication and Image Representation
Streaming video over HTTP with consistent quality
Proceedings of the 5th ACM Multimedia Systems Conference
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The authors formalize the description of the buffer-constrained adaptive quantization problem. For a given set of admissible quantizers used to code a discrete nonstationary signal sequence in a buffer-constrained environment, they formulate the optimal solution. They also develop slightly suboptimal but much faster approximations. These solutions are valid for any globally minimum distortion criterion, which is additive over the individual elements of the sequence. As a first step, they define the problem as one of constrained, discrete optimization and establish its equivalence to some of the problems studied in the field of integer programming. Forward dynamic programming using the Viterbi algorithm is shown to provide a way of computing the optimal solution. Then, they provide a heuristic algorithm based on Lagrangian optimization using an operational rate-distortion framework that, with computing complexity reduced by an order of magnitude, approaches the optimally achievable performance. The algorithms can serve as a benchmark for assessing the performance of buffer control strategies and are useful for applications such as multimedia workstation displays, video encoding for CD-ROMs, and buffered JPEG coding environments, where processing delay is not a concern but decoding buffer size has to be minimized