Development of Some Spatial-domain Preprocessing and Post-processing Algorithms for Better 2-D Up-scaling

Image super-resolution is an area of great interest in recent years and is extensively used in applications like video streaming, multimedia, internet technologies, consumer electronics, display and printing industries. Image super-resolution is a process of increasing the resolution of a given image without losing its integrity. Its most common application is to provide better visual effect after resizing a digital image for display or printing. One of the methods of improving the image resolution is through the employment of a 2-D interpolation. An up-scaled image should retain all the image details with very less degree of blurring meant for better visual quality. In literature, many efficient 2-D interpolation schemes are found that well preserve the image details in the up-scaled images; particularly at the regions with edges and fine details. Nevertheless, these existing interpolation schemes too give blurring effect in the up-scaled images due to the high frequency (HF) degradation during the up-sampling process. Hence, there is a scope to further improve their performance through the incorporation of various spatial domain pre-processing, post-processing and composite algorithms. Therefore, it is felt that there is sufficient scope to develop various efficient but simple pre-processing, post-processing and composite schemes to effectively restore the HF contents in the up-scaled images for various online and off-line applications. An efficient and widely used Lanczos-3 interpolation is taken for further performance improvement through the incorporation of various proposed algorithms. The various pre-processing algorithms developed in this thesis are summarized here. The term pre-processing refers to processing the low-resolution input image prior to image up-scaling. The various pre-processing algorithms proposed in this thesis are: Laplacian of Laplacian based global pre-processing (LLGP) scheme; Hybrid global pre-processing (HGP); Iterative Laplacian of Laplacian based global pre-processing (ILLGP); Unsharp masking based pre-processing (UMP); Iterative unsharp masking (IUM); Error based up-sampling(EU) scheme. The proposed algorithms: LLGP, HGP and ILLGP are three spatial domain preprocessing algorithms which are based on 4th, 6th and 8th order derivatives to alleviate nonuniform blurring in up-scaled images. These algorithms are used to obtain the high frequency (HF) extracts from an image by employing higher order derivatives and perform precise sharpening on a low resolution image to alleviate the blurring in its 2-D up-sampled counterpart. In case of unsharp masking based pre-processing (UMP) scheme, the blurred version of a low resolution image is used for HF extraction from the original version through image subtraction. The weighted version of the HF extracts are superimposed with the original image to produce a sharpened image prior to image up-scaling to counter blurring effectively. IUM makes use of many iterations to generate an unsharp mask which contains very high frequency (VHF) components. The VHF extract is the result of signal decomposition in terms of sub-bands using the concept of analysis filter bank. Since the degradation of VHF components is maximum, restoration of such components would produce much better restoration performance. EU is another pre-processing scheme in which the HF degradation due to image upscaling is extracted and is called prediction error. The prediction error contains the lost high frequency components. When this error is superimposed on the low resolution image prior to image up-sampling, blurring is considerably reduced in the up-scaled images. Various post-processing algorithms developed in this thesis are summarized in following. The term post-processing refers to processing the high resolution up-scaled image. The various post-processing algorithms proposed in this thesis are: Local adaptive Laplacian (LAL); Fuzzy weighted Laplacian (FWL); Legendre functional link artificial neural network(LFLANN). LAL is a non-fuzzy, local based scheme. The local regions of an up-scaled image with high variance are sharpened more than the region with moderate or low variance by employing a local adaptive Laplacian kernel. The weights of the LAL kernel are varied as per the normalized local variance so as to provide more degree of HF enhancement to high variance regions than the low variance counterpart to effectively counter the non-uniform blurring. Furthermore, FWL post-processing scheme with a higher degree of non-linearity is proposed to further improve the performance of LAL. FWL, being a fuzzy based mapping scheme, is highly nonlinear to resolve the blurring problem more effectively than LAL which employs a linear mapping. Another LFLANN based post-processing scheme is proposed here to minimize the cost function so as to reduce the blurring in a 2-D up-scaled image. Legendre polynomials are used for functional expansion of the input pattern-vector and provide high degree of nonlinearity. Therefore, the requirement of multiple layers can be replaced by single layer LFLANN architecture so as to reduce the cost function effectively for better restoration performance. With single layer architecture, it has reduced the computational complexity and hence is suitable for various real-time applications. There is a scope of further improvement of the stand-alone pre-processing and postprocessing schemes by combining them through composite schemes. Here, two spatial domain composite schemes, CS-I and CS-II are proposed to tackle non-uniform blurring in an up-scaled image. CS-I is developed by combining global iterative Laplacian (GIL) preprocessing scheme with LAL post-processing scheme. Another highly nonlinear composite scheme, CS-II is proposed which combines ILLGP scheme with a fuzzy weighted Laplacian post-processing scheme for more improved performance than the stand-alone schemes. Finally, it is observed that the proposed algorithms: ILLGP, IUM, FWL, LFLANN and CS-II are better algorithms in their respective categories for effectively reducing blurring in the up-scaled images

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods

Multi-scale edge-guided image gap restoration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The focus of this research work is the estimation of gaps (missing blocks) in digital images. To progress the research two main issues were identified as (1) the appropriate domains for image gap restoration and (2) the methodologies for gap interpolation. Multi-scale transforms provide an appropriate framework for gap restoration. The main advantages are transformations into a set of frequency and scales and the ability to progressively reduce the size of the gap to one sample wide at the transform apex. Two types of multi-scale transform were considered for comparative evaluation; 2-dimensional (2D) discrete cosines (DCT) pyramid and 2D discrete wavelets (DWT). For image gap estimation, a family of conventional weighted interpolators and directional edge-guided interpolators are developed and evaluated. Two types of edges were considered; ‘local’ edges or textures and ‘global’ edges such as the boundaries between objects or within/across patterns in the image. For local edge, or texture, modelling a number of methods were explored which aim to reconstruct a set of gradients across the restored gap as those computed from the known neighbourhood. These differential gradients are estimated along the geometrical vertical, horizontal and cross directions for each pixel of the gap. The edge-guided interpolators aim to operate on distinct regions confined within edge lines. For global edge-guided interpolation, two main methods explored are Sobel and Canny detectors. The latter provides improved edge detection. The combination and integration of different multi-scale domains, local edge interpolators, global edge-guided interpolators and iterative estimation of edges provided a variety of configurations that were comparatively explored and evaluated. For evaluation a set of images commonly used in the literature work were employed together with simulated regular and random image gaps at a variety of loss rate. The performance measures used are the peak signal to noise ratio (PSNR) and structure similarity index (SSIM). The results obtained are better than the state of the art reported in the literature

Enhanced quality reconstruction of erroneous video streams using packet filtering based on non-desynchronizing bits and UDP checksum-filtered list decoding

The latest video coding standards, such as H.264 and H.265, are extremely vulnerable in error-prone networks. Due to their sophisticated spatial and temporal prediction tools, the effect of an error is not limited to the erroneous area but it can easily propagate spatially to the neighboring blocks and temporally to the following frames. Thus, reconstructed video packets at the decoder side may exhibit significant visual quality degradation. Error concealment and error corrections are two mechanisms that have been developed to improve the quality of reconstructed frames in the presence of errors. In most existing error concealment approaches, the corrupted packets are ignored and only the correctly received information of the surrounding areas (spatially and/or temporally) is used to recover the erroneous area. This is due to the fact that there is no perfect error detection mechanism to identify correctly received blocks within a corrupted packet, and moreover because of the desynchronization problem caused by the transmission errors on the variable-length code (VLC). But, as many studies have shown, the corrupted packets may contain valuable information that can be used to reconstruct adequately of the lost area (e.g. when the error is located at the end of a slice). On the other hand, error correction approaches, such as list decoding, exploit the corrupted packets to generate several candidate transmitted packets from the corrupted received packet. They then select, among these candidates, the one with the highest likelihood of being the transmitted packet based on the available soft information (e.g. log-likelihood ratio (LLR) of each bit). However, list decoding approaches suffer from a large solution space of candidate transmitted packets. This is worsened when the soft information is not available at the application layer; a more realistic scenario in practice. Indeed, since it is unknown which bits have higher probabilities of having been modified during transmission, the candidate received packets cannot be ranked by likelihood. In this thesis, we propose various strategies to improve the quality of reconstructed packets which have been lightly damaged during transmission (e.g. at most a single error per packet). We first propose a simple but efficient mechanism to filter damaged packets in order to retain those likely to lead to a very good reconstruction and discard the others. This method can be used as a complement to most existing concealment approaches to enhance their performance. The method is based on the novel concept of non-desynchronizing bits (NDBs) defined, in the context of an H.264 context-adaptive variable-length coding (CAVLC) coded sequence, as a bit whose inversion does not cause desynchronization at the bitstream level nor changes the number of decoded macroblocks. We establish that, on typical coded bitstreams, the NDBs constitute about a one-third (about 30%) of a bitstream, and that the effect on visual quality of flipping one of them in a packet is mostly insignificant. In most cases (90%), the quality of the reconstructed packet when modifying an individual NDB is almost the same as the intact one. We thus demonstrate that keeping, under certain conditions, a corrupted packet as a candidate for the lost area can provide better visual quality compared to the concealment approaches. We finally propose a non-desync-based decoding framework, which retains a corrupted packet, under the condition of not causing desynchronization and not altering the number of expected macroblocks. The framework can be combined with most current concealment approaches. The proposed approach is compared to the frame copy (FC) concealment of Joint Model (JM) software (JM-FC) and a state-of-the-art concealment approach using the spatiotemporal boundary matching algorithm (STBMA) mechanism, in the case of one bit in error, and on average, respectively, provides 3.5 dB and 1.42 dB gain over them. We then propose a novel list decoding approach called checksum-filtered list decoding (CFLD) which can correct a packet at the bit stream level by exploiting the receiver side user datagram protocol (UDP) checksum value. The proposed approach is able to identify the possible locations of errors by analyzing the pattern of the calculated UDP checksum on the corrupted packet. This makes it possible to considerably reduce the number of candidate transmitted packets in comparison to conventional list decoding approaches, especially when no soft information is available. When a packet composed of N bits contains a single bit in error, instead of considering N candidate packets, as is the case in conventional list decoding approaches, the proposed approach considers approximately N/32 candidate packets, leading to a 97% reduction in the number of candidates. This reduction can increase to 99.6% in the case of a two-bit error. The method’s performance is evaluated using H.264 and high efficiency video coding (HEVC) test model software. We show that, in the case H.264 coded sequence, on average, the CFLD approach is able to correct the packet 66% of the time. It also offers a 2.74 dB gain over JM-FC and 1.14 dB and 1.42 dB gains over STBMA and hard output maximum likelihood decoding (HO-MLD), respectively. Additionally, in the case of HEVC, the CFLD approach corrects the corrupted packet 91% of the time, and offers 2.35 dB and 4.97 dB gains over our implementation of FC concealment in HEVC test model software (HM-FC) in class B (1920×1080) and C (832×480) sequences, respectively

Spatial Interpolation Algorithm For Error Concealment

In this paper, we propose a new spatial interpolation algorithm for Intra-Frame error concealment. The methodaims at interpolating areas in the image, which have been affected by packet loss. We have proposed an edge detectiontechnique to aid the bilinear interpolation. The edge detection scheme is based on designing a robust Hough transform-basedtechnique that is capable of systematically connecting edges irrespective of the number of edge points surrounding missingareas. The connected edges are used to divide the missing areas into different regions for interpolation along the directions ofeach detected line. Simulation results demonstrate that the proposed algorithm can recover the missing areas with a greateraccuracy, when compared with the bilinear interpolation technique