A new methodology for the determination of an objective metric for still image coding is reported. This methodology is applied to obtain a picture quality scale (PQS) for the coding of achromatic images over the full range of image quality defined by the subjective mean opinion score (MOS). This PQS takes into account the properties of visual perception for both global features and localized disturbances. The PQS closely approximates the MOS, with a correlation coefficient of more than 0.92, as compared to 0.57 obtained using the conventional weighted mean-square error (WMSE). Extensions and applications of the methodology and of the resulting metric are discussed.

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Objective picture quality scale (PQS) for image coding

A moving image representation format and encoding and decoding techniques. In the format, the image is represented as a series of layers, each layer comprising one or more maps containing information about the image. In decoding the information, the maps are combined in an order dictated by their ordinal depth in the image. Each layer comprises 1) an intensity map representing the intensity of the pixels in that layer, 2) an attenuation map by which the image intensities of the layers below it are multiplied while the intensity map corresponding to its layer is multiplied by its complement, 3) a delta map that describes the temporal derivative of the points in the corresponding intensity map, and 4) a velocity map which describes how all points in the layer are warped over time.

Layered representation for image coding

An image-coding technique, in which the discrete cosine transform (DCT) is combined with a classified vector quantization (CVQ), is presented. A DCT-transformed input block is classified according to the perceptual feature, partitioned into several smaller vectors, and then vector quantized. An efficient edge-oriented classifier employing the DCT coefficients as feature for classification is used to maintain the edge integrity in the reconstructed image. Based on a smaller geometric mean vector variance, a partition scheme in which 2-D DCT coefficients are divided into several smaller size vectors is also investigated. Because the distortion rate function (DRF) used is essential for the bit allocation algorithm to perform well, attempts have been made to modify the asymptotic DRF to estimate the performance of real VQs at low bit rates, and the modification is shown to be in good agreement with experimental results. Simulation results indicate that a good visual quality of the coded image in the range of 0.4 approximately 0.7 b/pixel is obtained. >

A transform domain classified vector quantizer for image coding

An image signal coding/decoding system comprises an encoder for encoding an image signal by using the adaptive quantization, and a decoder for decoding the coded image signal by using the adaptive inverse-quantization The encoder comprises an activity detection element for detecting, every blocks obtained by dividing an image signal every a specific time length, activities indicating degree of changes in the signal in the respective blocks; a quantization class determination element for determining class values of a plurality of quantization classes set in correspondence with respective stages of the adaptive quantization from the detected activities; and for outputting the determined class values as class information to the decoder, a filter element for filtering not only the class values but also class values at the peripheral blocks; and a quantization step width determination element for determining, every blocks, widths of quantization steps corresponding to a value obtained by multiplying these class values by fixed coefficients on the basis of class values subjected to filtering The decoder comprises a decode element for decoding coded data transmitted from the encoder by using the variable-length codes, and an inverse-quantization element for inverse-quantizing the variable-length decoded signal of the basis of class information transmitted from the quantization class determination element

Image signal coding/decoding system using adaptive quantization

A method to encode wavelet transformed (WT) images is described. WT decomposition of natural images preserves image structure within each subimage. Subsequent coding of this multiresolution representation can be achieved through techniques such as scalar/vector quantization (SQ/VQ). It is proposed that by transforming WT coefficients, a better result can be achieved in both compression ratio and output quality. Experimental results are presented to demonstrate the quality of the decoded images. >

Image coding using DCT of wavelet coefficients

A hybrid coding method based on estimation and detection of local features and classification of image data is presented in this paper. The local edge-orientations as well as the statistical properties are detected and estimated prior to the vector and the scalar quantization of the DCT coefficients. In a specific "feature prediction" process, both the local average grey-level which determines the DC coefficient and the local variance which contributes the ac energy distribution are estimated for each 8x8 image block by using the surrounding pixels of the block. In this way, the nonstationary image data are locally classified into sub-sources with each sub-source containing its own specific characteristics. The generally existent statistical dependences between the neighboring transform blocks are also exploited due to the feature prediction operation. The classification information which contains the local edge orientation and the local variance, determines the ac-energy distribution and consequently the vector forming of the ac coefficients in the vector quantization process. An adaptive scalar quantizer which is controlled by both the classification information and the channel buffer is then followed and clearly, the properties of the human visual system can be incorporated with this supplementary scalar quantization process to improve the coding performance. At a bit-rate round 1 bit/pixel, very good image quality can be observed with high signal-noise-ratio (up to 40dB).

Hybrid Image Coding Based On Local-Variant Source Models

Image Coding Using the Human Visual System

In this paper, two approaches of classified transform coding using vector quantization are presented and discussed The image data, which are known to be statistically uncertain, are classified before any further quantization and coding operations The local image activities can be ascertained either by using the neighboring decoded data e finite-state method) or by examining the current data (i e activity-detection method) The finite-state method, which is more preferable to he implemented using small transform blocks (typically 4 x 4) due to the fact that the inter-block correlations decrease for larger block dimensions, provides satisfied data classifications without requiring extra indication bits The activity-detection method is however more likely to have larger transform blocks in order to keep the number of the extra indication bits at a reasonable level Other practical considerations for both methods are also given

Two Approaches To Transform Coding Using Classified Vector Quantization

In this paper a specific data classification approach is presented which can be used in coders for transmitting video sequences. The image data is locally classified into four categories prior to the coding process which is then divided in four modes in order to exploit the masking effect of motion and to reach high performance. The four categories are: moving area containing an edge (data class Cf) moving area containing no single edge (class C/j) still area containing an edge (class C) and still area containing no single edge (class CE). This classification approach is based on the fact that the human visual system is especially sensitive to edge information and this in function of the motion behavior. Generally it is known that the edge information is critical to the human eyes. In the moving areas however the human eyes are less critical of the spatial details in comparison to that in the still areas because of the masking effect of motion. The data in the moving areas can be treated relatively coarsely but when edges are observed the quality criterion should still be raised appropriately. Using this four-mode coding method the subjective reproduction quality can be improved even in the case of low bit-rate applications. This classification approach is applied in the conventional motion compensating hybrid (DCT/DPCM) coder although it can also be with other coders.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image sequence coding using data classification

This paper presents an adaptive coding technique, based on a classification strategy, for color image sequence signal. Due to the classification process, carried out in the data domain, the non-stationary image data are partitioned into different classes characterized by the local spatial activities. The coding process is then optimized in each class. The low-passed image data are coded from two processing loops: a dynamic one in which an image block is obtained by using motion-estimation; a static loop in which a best matched vector from a vector quantization sub-codebook is chosen. The motion estimated data block, on the other hand, contains also the classification information. The difference data are then adaptively encoded by using the classification informations, and the properties of the human visual system can be incorporated in order to increase the performance.

G. Tu

Papers

Hybrid Image Coding Based On Local-Variant Source Models

Image Coding Using the Human Visual System

Two Approaches To Transform Coding Using Classified Vector Quantization

Image sequence coding using data classification

Adaptive Coding For Image Sequences In Transform Domain Based On A Classification Strategy