689 research outputs found
Wavelet Based Image Coding Schemes : A Recent Survey
A variety of new and powerful algorithms have been developed for image
compression over the years. Among them the wavelet-based image compression
schemes have gained much popularity due to their overlapping nature which
reduces the blocking artifacts that are common phenomena in JPEG compression
and multiresolution character which leads to superior energy compaction with
high quality reconstructed images. This paper provides a detailed survey on
some of the popular wavelet coding techniques such as the Embedded Zerotree
Wavelet (EZW) coding, Set Partitioning in Hierarchical Tree (SPIHT) coding, the
Set Partitioned Embedded Block (SPECK) Coder, and the Embedded Block Coding
with Optimized Truncation (EBCOT) algorithm. Other wavelet-based coding
techniques like the Wavelet Difference Reduction (WDR) and the Adaptive Scanned
Wavelet Difference Reduction (ASWDR) algorithms, the Space Frequency
Quantization (SFQ) algorithm, the Embedded Predictive Wavelet Image Coder
(EPWIC), Compression with Reversible Embedded Wavelet (CREW), the Stack-Run
(SR) coding and the recent Geometric Wavelet (GW) coding are also discussed.
Based on the review, recommendations and discussions are presented for
algorithm development and implementation.Comment: 18 pages, 7 figures, journa
Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
The next-generation Versatile Video Coding (VVC) standard introduces a new
Multi-Type Tree (MTT) block partitioning structure that supports Binary-Tree
(BT) and Ternary-Tree (TT) splits in both vertical and horizontal directions.
This new approach leads to five possible splits at each block depth and thereby
improves the coding efficiency of VVC over that of the preceding High
Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT)
partitioning with a single split per block depth. However, MTT also has brought
a considerable impact on encoder computational complexity. In this paper, a
two-stage learning-based technique is proposed to tackle the complexity
overhead of MTT in VVC intra encoders. In our scheme, the input block is first
processed by a Convolutional Neural Network (CNN) to predict its spatial
features through a vector of probabilities describing the partition at each 4x4
edge. Subsequently, a Decision Tree (DT) model leverages this vector of spatial
features to predict the most likely splits at each block. Finally, based on
this prediction, only the N most likely splits are processed by the
Rate-Distortion (RD) process of the encoder. In order to train our CNN and DT
models on a wide range of image contents, we also propose a public VVC frame
partitioning dataset based on existing image dataset encoded with the VVC
reference software encoder. Our proposal relying on the top-3 configuration
reaches 46.6% complexity reduction for a negligible bitrate increase of 0.86%.
A top-2 configuration enables a higher complexity reduction of 69.8% for 2.57%
bitrate loss. These results emphasis a better trade-off between VTM intra
coding efficiency and complexity reduction compared to the state-of-the-art
solutions
The contour tree image encoding technique and file format
The process of contourization is presented which converts a raster image into a discrete set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimises noticeable artifacts in the simplified image. The contour merging technique offers a complementary lossy compression system to the QDCT (Quantised Discrete Cosine Transform). The artifacts introduced by the two methods are very different; QDCT produces a general blurring and adds extra highlights in the form of overshoots, whereas contour merging sharpens edges, reduces highlights and introduces a degree of false contouring. A format based on the contourization technique which caters for most image types is defined, called the contour tree image format. Image operations directly on this compressed format have been studied which for certain manipulations can offer significant operational speed increases over using a standard raster image format. A couple of examples of operations specific to the contour tree format are presented showing some of the features of the new format.Science and Engineering Research Counci
A coarse representation of frames oriented video coding by leveraging cuboidal partitioning of image data
Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE
Space-frequency quantization for image compression with directionlets
The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm
Compressed Random-Access Trees for Spatially Coherent Data
International audienceAdaptive multiresolution hierarchies are highly efficient at representing spatially coherent graphics data. We introduce a framework for compressing such adaptive hierarchies using a compact randomly-accessible tree structure. Prior schemes have explored compressed trees, but nearly all involve entropy coding of a sequential traversal, thus preventing fine-grain random queries required by rendering algorithms. Instead, we use fixed-rate encoding for both the tree topology and its data. Key elements include the replacement of pointers by local offsets, a forested mipmap structure, vector quantization of inter-level residuals, and efficient coding of partially defined data. Both the offsets and codebook indices are stored as byte records for easy parsing by either CPU or GPU shaders. We show that continuous mipmapping over an adaptive tree is more efficient using primal subdivision than traditional dual subdivision. Finally, we demonstrate efficient compression of many data types including light maps, alpha mattes, distance fields, and HDR images
Ensemble Model Compression for~Fast and~Energy-Efficient Ranking on~{FPGAs}
We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine-learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks, we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on low-cost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9x up to 19.8x less energy than an equivalent multi-threaded CPU implementation
- …