This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity; 3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset [22] and the popular DTU MVS dataset [1]. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper.

This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient’s predicted significance degree. It includes two key dilation technologies: (1) controlling dilation process with context weights to reduce the output of insignificant coefficients and (2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict a coefficient’s significance degree more accurately, which can be used for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.

In this paper, we propose an embedded satellite image compression method using Weighted ZeroBlock Coding (WZBC) and optimal sorting. In order to reduce average codeword length, Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC) both encode significant block-sets with fixed-length bits, while WZBC assigns different-length bits to encode block-sets which contain different numbers of significant sub-blocks. In view of the context correlation among coefficients/blocks, WZBC employs a weight context to optimize the scanning order of the significance testing and the rate-distortion performance. Experimental results show that the proposed WZBC in binary coding mode provides excellent coding performance compared with those of SPECK and Set Partitioning In Hierarchical Trees (SPIHT) which use arithmetic coding, and can even closely approach that of JPEG2000. When arithmetic coding is extensively used, the proposed method has clear advantages.

In this paper, we propose an embedded satellite image compression method using the weighted zeroblock coding and optimal sorting. Unlike the conventional quad-tree coding methods such as Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC), in the proposed weighted zeroblock coding (WZBC), 1) we use the unfixed scanning order to code a significant block-set with fewer bits and achieve variable-length quad-tree coding; 2) we exploit the significance degree of sub-blocks which is predicted by a novel context-based weighted strategy to optimize the scanning order of the variable-length quad-tree coding and obtain the weighted zeroblock coding; 3) the rate-distortion performance of WZBC is also optimized by using the above mentioned weight. In the binary mode, the new method does not employ the arithmetic coding and has a fairly low complexity. Experimental results show that the proposed WZBC in the binary coding mode can provide an excellent coding performance compared with SPECK and Set Partitioning In Hierarchical Trees (SPIHT) with arithmetic coding, and can even closely approach JPEG2000. When the arithmetic coding is extensively used, the proposed method can obtain more obvious gain.

This paper proposes an image coding method based on adaptive downsampling which not only uses the pixel redundancy but also considers visual redundancy. At the encoder side, codec adaptively chooses some smooth regions of the original image to downsample, and then overlapped transform with selectivity, block DCT and adaptive-shape DCT (SA-DCT) are used against the image after being downsampled. For the incomplete transformed image, OB-SPECK is adopted to code. At the decoder side, in order to reduce the computational complexity, we use the simple cubic interpolation which not only is very suitable to the downsampled regions but also enhances greatly the real time of this coding system. Experimental results shows the proposed method outperforms JPEG2000, SPECK, SPIHT, and LT+SPECK at low bit rates.