The availability of increased computational power and the proliferation of the Internet
have facilitated the production and distribution of unauthorized copies of
multimedia information. As a result, the problem of copyright protection has attracted
the interest of the worldwide scientific and business communities. The most
promising solution seems to be the watermarking process where the original data
is marked with ownership information hidden in an imperceptible manner in the
original signal. Compared to embedding watermarks into still images, hiding data
in audio is much more challenging due to the extreme sensitivity of the human
auditory system to changes in the audio signal. Understanding of the human perception
processes and including them in effective psychoacoustic models is the key to
successful watermarking. Aside from psychoacoustic modeling, synchronization is
also an important component for a successful watermarking system. In order to
recover the embedded watermark from the watermarked signal, the detector has to
know the beginning location of the embedded watermark first.
In this book, we focus on those two issues. We propose a psychoacoustic model
which is based on the discrete wavelet packet transform (DWPT). This model
takes advantage of the flexibility of DWPT decomposition to closely approximate
the critical bands and provides precise masking thresholds, resulting in increased
extent of inaudible spectrum and reduction of sum to signal masking ratio (SSMR)
compared to the existing competing techniques. The proposed psychoacoustic
model has direct applications to digital perceptual audio coding as well as digital
For digital perceptual audio coding, the greater extent of inaudible spectrum
provided by the psychoacoustic model results more audio samples to be quantized
to zero, leading to a decreased compression bit rate. The reduction of SSMR on the
other hand, allows a coarser quantization step, which further cuts the necessary bits
for audio representation in the audible spectrum areas. In other words, the audio
compressed with the proposed digital perceptual codec achieves better subjective
quality than an existing coding standard when operating at the same information
rate, which is proven by the subjective listening test.
With the best practices and optimization techniques you’ll learn in this book, you can optimize performance without...
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