ISO IEC JTC1 SC29 WG1 N4548 Date March 31 2008 ISO IEC JTC 1 SC 29 WG 1 ITU T SG16 Coding of Still Pictures JBIG Joint Bi level Image Experts Group JPEG JointPhotographic Experts Group TITLE Proposal for objective distortion metrics for AIC standardization SOURCE J J Hwang S G Cho hwang sgcho kunsan ac kr Kunsan National Univ Y Huh yhuh keri re kr KERI PROJECT AIC STATUS Proposal REQUESTED ACTION For discussion at WG1 Meeting March 2008 San Francisco DISTRIBUTION WG 1 Contact ISO IEC JTC 1 SC 29 WG 1 Convener Dr Daniel T Lee Yahoo Asia 10 Hysan Avenue Sunning Plaza Ste 2802 Causeway Bay Hong Kong Yahoo Inc 701 First Avenue Sunnyvale California 94089 USA Tel 1 408 349 7051 852 2882 3898 Fax 1 253 830 0372 E mail dlee yahoo inc com 1 1 Introduction The goal of image quality assessment is to accurately measure how much the reconstruction image is degraded by comparing with its original and utilize the results to design an optimum image codec Widely used assessment metrics for objective quality measurement are Peak Signal toNoise Ratio PSNR and Mean Square Error MSE These metrics have two drawbacks First only one global distortion is calculated so that any specific artifacts may not be measured Second they are not considering the human visual properties that should be observed by human viewer Recently Structural SIMilarity SSIM index is proposed under the assumption that the Human Visual System HVS is highly adapted for extracting structural information from a scene However it also does not detect individual artifacts but provides a global ranking Wang2004 Compression and transmission of digital image and video entail a variety of characteristic artifacts and distortions e g blockiness blurriness ringing Yuen1998 Some of them may be caused by the compression algorithm while the others as a consequence of transmission errors and various video conversions Typical artifacts are Blockiness or the blocking effect refers to a block pattern in the compressed sequence It is due to the independent quantization of individual blocks usually 8 8 pixels in blockbased DCT coding schemes Blur or the blurriness effect is characterized by the loss of fine detail and the smearing of edges in the video It is typically caused by high frequency attenuation at some stage of the recording or encoding process Wavelet based encoders also cause blurry artifacts Ringing is fundamentally associated with Gibbs phenomenon and is thus most evident along high contrast edges in otherwise smooth area It is a direct result of quantization leading to high frequency irregularities in the reconstruction Ringing occurs with both luminance and chroma components False edges are a consequence of the transfer of block boundary discontinuities due to the blocking effect from reference frames into the predicted frame by motion compensation MC Color bleeding is the smearing of the color between areas of strongly differing chrominance It results from the suppression of high frequency coefficients of the chroma components Due to chroma sub sampling color bleeding extends over an entire block Aliasing can be noticed when the content of the scene is above the Nyquist rate either spatially or temporally When transporting media over noisy channels packet loss or packet delay can occur Such losses or delays can affect both the semantics and the syntax of the media stream The distortion metrics can be categorized into 1 in terms of the number of measurement results composite metrics e g PSNR SSIM and component metrics e g blocking blurring etc 2 in terms of measurement region in an image global and local 3 and others subjective objective FR NR Conventional image coders mostly rely on composite and global distortion metrics Since the composite metric can be decomposed into a number of components new image coders based on component wise distortion metrics seems to be feasible By analyzing and reducing any 2 component s of coding artifacts it is able to design more efficient encoder By standardizing the distortion metrics it may be used for Transcoding for existing standards Rate distortion control for existing and new standards Quality requirement for ne w standards Under these background and motivations we reviewed several No Reference NR and Full Reference FR objective quality metrics and developed some useful algorithms in order to take into account human visual properties and subjective measurement results 2 Distortion Metrics We have investigated in three types of artifacts namely blockiness blurriness and ringing As already known the blockiness is the main artifact in block based coder such as JPEG MPEG x and H 26x series while the blurriness and ringing artifacts occur in the Waveletbased JPEG2000 encoder In this section we reviewed several FR NR metrics to measure perceptual artifacts in image or video and presented some modified version of both FR and NR metrics 2 1 Blocking metrics 2 1 1 Entropy based blocking metric In general the blocking artifact is defined as the discontinuities found across block boundary It is more apparent in smooth textured region where local variance is lower than others Shao2007 From these properties when designing a new metric we assumed that the blocking dominant region produces lower local variance and entropy than that in original image We developed the entropy based metric as shown in Figure 1 First the artifact regions are extracted by comparing variance and entropy of test and original image Next we find the boundary of blocking region based on morphologic operation Finally the blocking artifact is measured in a similar fashion of the extrapolation based blocking metric in Caviedes2003 For the NR measurement we compare local entropy with specified value instead of reference image This metric shows better measurement results in terms of quality and correlation factor along with perceived distortion DMOS Differential Mean Opinion Score which is depicted in Figure 13 However threshold value of entropy and variance is a practical problem to decide whether blocking happens or not The blocking artifacts can be measured as the number of 8 pixel edges found on the 8 8 grid overlaying the image To differentiate block edges from natural edges it is assumed that natural edges are strong i e very steep transitions while block edges are weak and regularly spaced 3 Figure 1 Flow chart of entropy based blocking metric a b c d Figure 2 Example of entropy based blocking metric a test image b blocking region c
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