Improving MPEG-4 coding performance by jointly optimising compression and blocking effect elimination

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringAccepted ManuscriptPublishe

Edge-enhancing image smoothing.

Xu, Yi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 62-69).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Organization --- p.4Chapter 2 --- Background and Motivation --- p.7Chapter 2.1 --- ID Mondrian Smoothing --- p.9Chapter 2.2 --- 2D Formulation --- p.13Chapter 3 --- Solver --- p.16Chapter 3.1 --- More Analysis --- p.20Chapter 4 --- Edge Extraction --- p.26Chapter 4.1 --- Related work --- p.26Chapter 4.2 --- Method and Results --- p.28Chapter 4.3 --- Summary --- p.32Chapter 5 --- Image Abstraction and Pencil Sketching --- p.35Chapter 5.1 --- Related Work --- p.35Chapter 5.2 --- Method and Results --- p.36Chapter 5.3 --- Summary --- p.40Chapter 6 --- Clip-Art Compression Artifact Removal --- p.41Chapter 6.1 --- Related work --- p.41Chapter 6.2 --- Method and Results --- p.43Chapter 6.3 --- Summary --- p.46Chapter 7 --- Layer-Based Contrast Manipulation --- p.49Chapter 7.1 --- Related Work --- p.49Chapter 7.2 --- Method and Results --- p.50Chapter 7.2.1 --- Edge Adjustment --- p.51Chapter 7.2.2 --- Detail Magnification --- p.54Chapter 7.2.3 --- Tone Mapping --- p.55Chapter 7.3 --- Summary --- p.56Chapter 8 --- Conclusion and Discussion --- p.59Bibliography --- p.6

Efficient Implementation of Image Compression-Postprocessing Algorithm Using a Digital Signal Processor

In this thesis, an attempt has been made to develop a fast way to implement a post-processing algorithm for image compression. All the previous tests for this postprocessing algorithm, which we will present, have been only software based and did not consider the time parameter. For this purpose a new algorithm is used to compute the 2-D DCT transform. This change made the process a lot faster on a Spare 5 workstation. We have then decided to further increase the speed of the post-processing scheme by implementing it on the ADSP21020 chip. The results show that such a chip can achieve a speed increase and that ifthe code is optimized a faster processing is even reachable

A learning-by-example method for reducing BDCT compression artifacts in high-contrast images.

Wang, Guangyu.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- BDCT Compression Artifacts --- p.1Chapter 1.2 --- Previous Artifact Removal Methods --- p.3Chapter 1.3 --- Our Method --- p.4Chapter 1.4 --- Structure of the Thesis --- p.4Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Image Compression --- p.6Chapter 2.2 --- A Typical BDCT Compression: Baseline JPEG --- p.7Chapter 2.3 --- Existing Artifact Removal Methods --- p.10Chapter 2.3.1 --- Post-Filtering --- p.10Chapter 2.3.2 --- Projection onto Convex Sets --- p.12Chapter 2.3.3 --- Learning by Examples --- p.13Chapter 2.4 --- Other Related Work --- p.14Chapter 3 --- Contamination as Markov Random Field --- p.17Chapter 3.1 --- Markov Random Field --- p.17Chapter 3.2 --- Contamination as MRF --- p.18Chapter 4 --- Training Set Preparation --- p.22Chapter 4.1 --- Training Images Selection --- p.22Chapter 4.2 --- Bit Rate --- p.23Chapter 5 --- Artifact Vectors --- p.26Chapter 5.1 --- Formation of Artifact Vectors --- p.26Chapter 5.2 --- Luminance Remapping --- p.29Chapter 5.3 --- Dominant Implication --- p.29Chapter 6 --- Tree-Structured Vector Quantization --- p.32Chapter 6.1 --- Background --- p.32Chapter 6.1.1 --- Vector Quantization --- p.32Chapter 6.1.2 --- Tree-Structured Vector Quantization --- p.33Chapter 6.1.3 --- K-Means Clustering --- p.34Chapter 6.2 --- TSVQ in Artifact Removal --- p.35Chapter 7 --- Synthesis --- p.39Chapter 7.1 --- Color Processing --- p.39Chapter 7.2 --- Artifact Removal --- p.40Chapter 7.3 --- Selective Rejection of Synthesized Values --- p.42Chapter 8 --- Experimental Results --- p.48Chapter 8.1 --- Image Quality Assessments --- p.48Chapter 8.1.1 --- Peak Signal-Noise Ratio --- p.48Chapter 8.1.2 --- Mean Structural SIMilarity --- p.49Chapter 8.2 --- Performance --- p.50Chapter 8.3 --- How Size of Training Set Affects the Performance --- p.52Chapter 8.4 --- How Bit Rates Affect the Performance --- p.54Chapter 8.5 --- Comparisons --- p.56Chapter 9 --- Conclusion --- p.61Chapter A --- Color Transformation --- p.63Chapter B --- Image Quality --- p.64Chapter B.1 --- Image Quality vs. Quantization Table --- p.64Chapter B.2 --- Image Quality vs. Bit Rate --- p.66Chapter C --- Arti User's Manual --- p.68Bibliography --- p.7

Reduction of blocking artifacts using side information

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 95-96).Block-based image and video coding systems are used extensively in practice. In low bit-rate applications, however, they suffer from annoying discontinuities, called blocking artifacts. Prior research shows that incorporating systems that reduce blocking artifacts into codecs is useful because visual quality is improved. Existing methods reduce blocking artifacts by applying various post-processing techniques to the compressed image. Such methods require neither any modification to current encoders nor an increase in the bit-rate. This thesis examines a framework where blocking artifacts are reduced using side information transmitted from the encoder to the decoder. Using side information enables the use of the original image in deblocking, which improves performance. Furthermore, the computational burden at the decoder is reduced. The principal question that arises is whether the gains in performance of this choice can compensate for the increase in the bit-rate due to the transmission of side information. Experiments are carried out to answer this question with the following sample system: The encoder determines block boundaries that exhibit blocking artifacts as well as filters (from a predefined set of filters) that best deblock these block boundaries.(cont.) Then it transmits side information that conveys the determined block boundaries together with their selected filters to the decoder. The decoder uses the received side information to perform deblocking. The proposed sample system is compared against an ordinary coding system and a post-processing type deblocking system with the bit-rate of these systems being equal to the overall bit-rate (regular encoding bits + side information bits) of the proposed system. The results of the comparisons indicate that, both for images and video sequences, the proposed system can perform better in terms of both visual quality and PSNR for some range of coding bit-rates.by Fatih Kamisli.S.M

DC coefficient restoration for transform image coding.

by Tse, Fu Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 155-[63]).Acknowledgment --- p.iiiAbstract --- p.ivContents --- p.viList of Tables --- p.xList of Figures --- p.xiiNotations --- p.xviiChapter 1 --- Introduction --- p.1Chapter 1.1 --- DC coefficient restoration --- p.1Chapter 1.2 --- Model based image compression --- p.5Chapter 1.3 --- The minimum edge difference criterion and the existing estima- tion schemes --- p.7Chapter 1.3.1 --- Fundamental definitions --- p.8Chapter 1.3.2 --- The minimum edge difference criterion --- p.9Chapter 1.3.3 --- The existing estimation schemes --- p.10Chapter 1.4 --- Thesis outline --- p.14Chapter 2 --- A mathematical description of the DC coefficient restoration problem --- p.17Chapter 2.1 --- Introduction --- p.17Chapter 2.2 --- Mathematical formulation --- p.18Chapter 2.3 --- Properties of H --- p.22Chapter 2.4 --- Analysis of the DC coefficient restoration problem --- p.22Chapter 2.5 --- The MED criterion as an image model --- p.25Chapter 2.6 --- Summary --- p.27Chapter 3 --- The global estimation scheme --- p.29Chapter 3.1 --- Introduction --- p.29Chapter 3.2 --- the global estimation scheme --- p.30Chapter 3.3 --- Theory of successive over-relaxation --- p.34Chapter 3.3.1 --- Introduction --- p.34Chapter 3.3.2 --- Gauss-Seidel iteration --- p.35Chapter 3.3.3 --- Theory of successive over-relaxation --- p.38Chapter 3.3.4 --- Estimation of optimal relaxation parameter --- p.41Chapter 3.4 --- Using successive over-relaxation in the global estimation scheme --- p.43Chapter 3.5 --- Experiments --- p.48Chapter 3.6 --- Summary --- p.49Chapter 4 --- The block selection scheme --- p.52Chapter 4.1 --- Introduction --- p.52Chapter 4.2 --- Failure of the minimum edge difference criterion --- p.53Chapter 4.3 --- The block selection scheme --- p.55Chapter 4.4 --- Using successive over-relaxation with the block selection scheme --- p.57Chapter 4.5 --- Practical considerations --- p.58Chapter 4.6 --- Experiments --- p.60Chapter 4.7 --- Summary --- p.61Chapter 5 --- The edge selection scheme --- p.65Chapter 5.1 --- Introduction --- p.65Chapter 5.2 --- Edge information and the MED criterion --- p.66Chapter 5.3 --- Mathematical formulation --- p.70Chapter 5.4 --- Practical Considerations --- p.74Chapter 5.5 --- Experiments --- p.76Chapter 5.6 --- Discussion of edge selection scheme --- p.78Chapter 5.7 --- Summary --- p.79Chapter 6 --- Performance Analysis --- p.81Chapter 6.1 --- Introduction --- p.81Chapter 6.2 --- Mathematical derivations --- p.82Chapter 6.3 --- Simulation results --- p.92Chapter 6.4 --- Summary --- p.96Chapter 7 --- The DC coefficient restoration scheme with baseline JPEG --- p.97Chapter 7.1 --- Introduction --- p.97Chapter 7.2 --- General specifications --- p.97Chapter 7.3 --- Simulation results --- p.101Chapter 7.3.1 --- The global estimation scheme with the block selection scheme --- p.101Chapter 7.3.2 --- The global estimation scheme with the edge selection scheme --- p.113Chapter 7.3.3 --- Performance comparison at the same bit rate --- p.121Chapter 7.4 --- Computation overhead using the DC coefficient restoration scheme --- p.134Chapter 7.5 --- Summary --- p.134Chapter 8 --- Conclusions and Discussions --- p.136Chapter A --- Fundamental definitions --- p.144Chapter B --- Irreducibility by associated directed graph --- p.146Chapter B.1 --- Irreducibility and associated directed graph --- p.146Chapter B.2 --- Derivation of irreducibility --- p.147Chapter B.3 --- Multiple blocks selection --- p.149Chapter B.4 --- Irreducibility with edge selection --- p.151Chapter C --- Sample images --- p.153Bibliography --- p.15