Advances in image compression, storage, and display technologies have made high-quality images and videos widely accessible. At this level of quality, distinguishing between compressed and original content becomes difficult, highlighting the need for assessment methodologies that are sensitive to even the smallest visual quality differences. Conventional subjective visual quality assessments often use absolute category rating scales, ranging from “excellent” to “bad”. While suitable for evaluating more pronounced distortions, these scales are inadequate for detecting subtle visual differences. The JPEG standardization project AIC is currently developing a subjective image quality assessment methodology for high-fidelity images. This paper presents the proposed assessment methods, a dataset of high-quality compressed images, and their corresponding crowdsourced visual quality ratings. It also outlines a data analysis approach that reconstructs quality scale values in just noticeable difference (JND) units. The assessment method uses boosting techniques on visual stimuli to help observers detect compression artifacts more clearly. This is followed by a rescaling process that adjusts the boosted quality values back to the original perceptual scale. This reconstruction yields a fine-grained, high-precision quality scale in JND units, providing more informative results for practical applications. The dataset and code to reproduce the results will be available at https://github.com/jpeg-aic/dataset-BTC-PTC-24.

Summary form only given. This poster contains two contributions to very-low-bitrate video coding. First, we show that in contrast to common practice incremental techniques for rate-distortion optimization such as the generalized BFOS algorithm may clearly outperform the standard technique based on Lagrangian multipliers. This is relevant in cases where the computation of RD points has a low complexity. An implementation independent performance measure is used for comparison and run-time experiments are provided. Second, we report on recent progress of our ongoing research evaluating the prospects of adaptive vector quantization (AVQ) for very-low-bitrate video coding. In contrast to conventional state-of-the-art video coding based on entropy coding of motion compensated residual frames in the frequency domain, adaptive vector quantization offers the potential to adapt its codebooks to the changing statistics of image sequences. The basic building blocks of our current AVQ video codec are: (1) block-based coding in the wavelet domain where wavelet coefficients correspond to (overlapping) spatial regions; (2) hierarchical organization of the wavelet coefficients using quadtree structures; (3) three way coding mode decision for each block (block replenishment, product code vector quantization, new VQ block with codebook update); and (4) rigorous rate/distortion optimization for all coding choices (image partition and block coding mode). This video codec does not apply motion compensation, however. A comparison with standard transform coding (H.263) shows that in spite of the improvements of our coder over previously published AVQ video coders it still shows a performance gap of about 1 dB for some test sequences.

Summary form only given. We present a new AVQ-based video coder for very low bitrates. To encode a block from a frame, the encoder offers three modes: (1) a block from the same position in the last frame can be taken; (2) the block can be represented with a vector from the codebook; or (3) a new vector, that sufficiently represents a block, can be inserted into the codebook. For mode 2 a mean-removed VQ scheme is used. The decision on how blocks are encoded and how the codebook is updated is done in an rate-distortion (R-D) optimized fashion. The codebook of shape blocks is updated once per frame. First results for an implementation of such a scheme have been reported previously. Here we extend the method to incorporate a wavelet image transform before coding in order to enhance the compression performance. In addition the rate-distortion optimization is comprehensively discussed. Our R-D optimization is based on an efficient convex-hull computation. This method is compared to common R-D optimizations that use a Lagrangian multiplier approach. In the discussion of our R-D method we show the similarities and differences between our scheme and the generalized threshold replenishment (GTR) method of Fowler et al. (1997). Furthermore, we demonstrate that the translation of our R-D optimized AVQ into the wavelet domain leads to an improved coding performance. We present coding results that show that one can achieve the same encoding quality as with comparable standard transform coding (H.263). In addition we offer an empirical analysis of the short- and long-term behavior of the adaptive codebook. This analysis indicates that the AVQ method uses the vectors in its codebook for some kind of long-term prediction.

Mean shape-gain vector quantization (MSGVQ) is extended to include negative gains and square isometries. Square isometries together with a classification technique based on average block intensities enable us to enlarge the MSGVQ codebook size without any additional storage requirements while keeping the complexity of both the codebook generation and the encoding manageable. Variable rate codes are obtained with a quadtree segmentation based on a rate-distortion criterion. Experimental results show that our scheme performs favorably when compared to previous product code techniques or quadtree based VQ methods.

In fractal image compression the encoding step is computationally expensive. A large number of sequential searches through a list of domains (portions of the image) are carried out while trying to find the best match for another image portion. Our theory developed here shows that this basic procedure of fractal image compression is equivalent to multi-dimensional nearest neighbor search. This result is useful for accelerating the encoding procedure in fractal image compression. The traditional sequential search takes linear time whereas the nearest neighbor search can be organized to require only logarithmic time. The fast search has been integrated into an existing state-of-the-art classification method thereby accelerating the searches carried out in the individual domain classes. In this case we record acceleration factors from 1.3 up to 11.5 depending on image and domain pool size with negligible or minor degradation in both image quality and compression ratio. Furthermore, as compared to plain classification our method is demonstrated to be able to search through larger portions of the domain pool without increased the computation time.