Decoded Picture Quality Research Articles

Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.

An Improved Neural Image Compression Approach With Cluster Based Pre-Processing

The convergence time for training back propagation neural network for image compression is slow as compared to other traditional image compression techniques. This article proposes a pre-processing technique i.e. Pre-processed Back propagation neural image compression (PBN) with an enhancement in performance measures like better convergence time with respect to decoded picture quality and compression ratios as compared to simple back-propagation based image compression and other image coding techniques for color images.

Design of a neuro fuzzy model for image compression in wavelet domain

Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in the data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector quantization, wavelet-based sub band decomposition and neural network. The vector quantization is often used when high compression ratios are required. The implementation consists of three steps: first, the image is decomposed into a set of sub bands with different resolutions corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. In the second step, wavelet coefficients corresponding to the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. Finally, the result of the second step was used as input to fuzzy vector quantizer. Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005; Premaraju, 1996).

Systematic Lossy Error Protection of Video Signals

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper proposes a scheme called Systematic Lossy Error Protection (SLEP) for robust transmission of video signals over packet erasure channels. The systematic portion of the transmission consists of a conventionally encoded video bit stream which is transmitted without channel coding. An additional bit stream generated by Wyner–Ziv encoding of the video signal is transmitted for error resilience. In the event of packet loss, this supplementary bit stream is decoded and allows the recovery of a coarsely quantized video signal, which is displayed in lieu of the lost portions of the primary video signal. The quantization mismatch results in a small, controlled loss in picture quality, but a drastic reduction in picture quality is avoided. An implementation of the SLEP system using the state-of-the-art H.264/AVC standard codec is described. Specifically, H.264/AVC redundant slices are used in conjunction with Reed–Solomon coding to generate the Wyner–Ziv bit stream. The received video quality is modeled as a function of the bit rates of the primary and redundant descriptions and the error resilience bit rate. The model is used to optimize the video quality delivered by SLEP. Via theoretical analysis and experimental simulation, it is shown that SLEP provides a flexible tradeoff between error resilience and decoded picture quality. By allowing the quality to degrade gracefully over a wider range of packet loss rates, SLEP mitigates the precipitous drop in picture quality suffered by traditional FEC-based systems. </para>

Dynamic Multi-mode Switching Error Concealment Algorithm for H.264/AVC Video Applications

In video communication based consumer device applications, the compressed video is extremely fragile to transmission errors due to channel noise. Error concealment (EC) is an efficient way to recover a damaged video sequence. The existing EC algorithms for compressed video typically apply uniform EC mode to entire video sequence regardless of frame features, or lack in sensible mode-switching considerations. This paper proposes a new dynamic multi-mode switching (DMS) EC algorithm. The proposed algorithm provides several EC modes for intra- and inter- frames. The proposed smart decoder is able to adaptively apply these modes in different situations. The mode switching in DMS depends on the spatial/temporal correlation, estimated edge features of the corrupted Macroblock (MB), the smoothness of the reconstructed MB and etc. The proposed DMS algorithm has been evaluated and compared with 3 classical EC algorithms. The experimental results show that the DMS algorithm can achieve significant gains in peak signal noise ratio (PSNR) and therefore improves the decoded picture quality in video applications.

Distortion variation minimization in real-time video coding

In this paper, we review the rate distortion tradeoff issues in real-time video coding and introduce a minimum variation (MINVAR) distortion criterion based approach. The MINVAR based rate distortion tradeoff framework provides a local optimization strategy as a rate control mechanism in real-time video coding applications by minimizing the distortion variation while the corresponding bit rate fluctuation is limited by utilizing the encoder buffer. The proposed approach aims to achieve a smooth decoded picture quality for pleasing human visual experience. The performance of the proposed method is evaluated with H.264. The experimental results demonstrate that using the proposed approach, the decoded picture quality is smoother than the traditional H.264 joint model (JM) rate control without sacrificing global quality such that a better subjective visual quality is guaranteed.

Joint texture-shape optimization for MPEG-4 multiple video objects

In this paper, we present a uniform framework for optimal multiple video object bit allocation in MPEG-4. We combine the rate-distortion (R-D) models for the texture and shape information of arbitrarily shaped video objects to develop the joint texture-shape R-D models. The dynamic programming technique is applied to optimize the bit allocation for the multiple video objects. The simulation results demonstrate that the proposed joint texture-shape optimization algorithm outperforms the MPEG-4 verification model on the decoded picture quality.

Improved rate control for MPEG-4 video transport over wireless channel

Video streaming over wireless channel is challenging due to a number of factors such as limited bandwidth and loss sensitivity. In this paper, we develop a novel rate control algorithm for MPEG-4 video coding. Unlike traditional rate control schemes, we jointly consider the encoding complexity variation and buffer variation as well as human visual properties to optimize the rate control efficiency. We also analyze the sensitivity of a macroblock (MB) as a result of bit errors and calculate its error sensitivity metric. This metric is used in unequal error protection of the MB. Simulation results show that proposed approach can improve the decoded picture quality in wireless video coding and transmission.

Multiple description coding of motion fields for robust video transmission

In many video-coding standards, the motion vector field is one of the most important data in the compressed bitstream, and its loss can lead to severe degradation in the decoded picture quality. We propose the multiple description motion coding (MDMC) algorithm to enhance the robustness of the motion vector field against transmission errors. In MDMC, the motion vector field is encoded into two descriptions, which are transmitted over distinct channels to the decoder. The decoder is designed to provide an acceptable quality prediction image, even if one of the descriptions is lost during the transmission. Moreover, the decoder can reconstruct a higher quality prediction image, when both the descriptions are received without error. A complete multiple description video coder, based on the MDMC, is implemented by modifying the syntax of the H.263 standard, and tested intensively in a realistic error-prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the H.263 coder in the error-prone environment.

Image compression using hybrid neural networks combining the auto-associative multi-layer perceptron and the self-organizing feature map

A new image compression technique is presented using hybrid neural networks that combine two different learning networks, the auto-associative multi-layer perceptron (AMLP) and the self-organizing feature map (SOFM). The neural networks simultaneously perform dimensionality reduction with the AMLP and categorization with the SOFM to compress image data. Two hybrid neural networks forming parallel and serial architectures are examined through theoretical analysis and computer simulation. The parallel structure network reduces the dimensionality of input pattern vectors by mapping them to different hidden layers of the AMLP selected by winner-take-all units of the SOFM. The serial structure network categorizes the input pattern vectors into several classes representing prototype vectors. Both the serial and parallel structures are combinations of the AMLP and SOFM networks. These hybrid neural networks achieve clear performance improvement with respect to decoded picture quality and compression ratios, compared to existing image compression techniques. >