13 research outputs found
Color space adaptation for video coding
Processament d'imatges abans de ser codificades pel codificador HEVC amb la finalitat d'augmentar la qualitat i la fidelitat.[ANGLÈS] Project on the objective and subjective improvements by pre-processing images to be encoded into a video.[CASTELLÀ] Proyecto sobre la repercusión en la mejora de calidad objetiva y subjetiva del pre-procesado de imágenes a codificar con vÃdeo.[CATALÀ] Projecte sobre la repercussió en la millora de la qualitat objectiva i subjectiva del pre-processament d'imatges a codificar amb vÃdeo
Data Hiding and Its Applications
Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
Remote Sensing Data Compression
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin
On the design of fast and efficient wavelet image coders with reduced memory usage
Image compression is of great importance in multimedia systems and
applications because it drastically reduces bandwidth requirements for
transmission and memory requirements for storage. Although earlier
standards for image compression were based on the Discrete Cosine
Transform (DCT), a recently developed mathematical technique, called
Discrete Wavelet Transform (DWT), has been found to be more efficient
for image coding.
Despite improvements in compression efficiency, wavelet image coders
significantly increase memory usage and complexity when compared with
DCT-based coders. A major reason for the high memory requirements is
that the usual algorithm to compute the wavelet transform requires the
entire image to be in memory. Although some proposals reduce the memory
usage, they present problems that hinder their implementation. In
addition, some wavelet image coders, like SPIHT (which has become a
benchmark for wavelet coding), always need to hold the entire image in
memory.
Regarding the complexity of the coders, SPIHT can be considered quite
complex because it performs bit-plane coding with multiple image scans.
The wavelet-based JPEG 2000 standard is still more complex because it
improves coding efficiency through time-consuming methods, such as an
iterative optimization algorithm based on the Lagrange multiplier
method, and high-order context modeling.
In this thesis, we aim to reduce memory usage and complexity in
wavelet-based image coding, while preserving compression efficiency. To
this end, a run-length encoder and a tree-based wavelet encoder are
proposed. In addition, a new algorithm to efficiently compute the
wavelet transform is presented. This algorithm achieves low memory
consumption using line-by-line processing, and it employs recursion to
automatically place the order in which the wavelet transform is
computed, solving some synchronization problems that have not been
tackled by previous proposals. The proposed encodeOliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826Palanci
Macroblock level rate and distortion estimation applied to the computation of the Lagrange multiplier in H.264 compression
The optimal value of Lagrange multiplier, a trade-off factor between the conveyed rate and distortion measured at the signal reconstruction has been a fundamental problem of rate distortion theory and video compression in particular.
The H.264 standard does not specify how to determine the optimal combination of the quantization parameter (QP) values and encoding choices (motion vectors, mode decision). So far, the encoding process is still subject to the static value of Lagrange multiplier, having an exponential dependence on QP as adopted by the scientific community. However, this static value cannot accommodate the diversity of video sequences. Determining its optimal value is still a challenge for current research.
In this thesis, we propose a novel algorithm that dynamically adapts the Lagrange multiplier to the video input by using the distribution of the transformed residuals at the macroblock level, expected to result in an improved compression performance in the rate-distortion space.
We apply several models to the transformed residuals (Laplace, Gaussian, generic probability density function) at the macroblock level to estimate the rate and distortion, and study how well they fit the actual values. We then analyze the benefits and drawbacks of a few simple models (Laplace and a mixture of Laplace and Gaussian) from the standpoint of acquired compression gain versus visual improvement in connection to the H.264 standard.
Rather than computing the Lagrange multiplier based on a model applied to the whole frame, as proposed in the state-of-the-art, we compute it based on models applied at the macroblock level. The new algorithm estimates, from the macroblock’s transformed residuals, its rate and distortion and then combines the contribution of each to compute the frame’s Lagrange multiplier.
The experiments on various types of videos showed that the distortion calculated at the macroblock level approaches the real one delivered by the reference software for most sequences tested, although a reliable rate model is still lacking especially at low bit rate. Nevertheless, the results obtained from compressing various video sequences show that the proposed method performs significantly better than the H.264 Joint Model and is slightly better than state-of-the-art methods