4bb735f7966eedf414aa8e24533967cffd5bbcdb
18 Commits
| Author | SHA1 | Message | Date | |
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 Alexander Suvorov
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660322d3a6 |
Add compression support for ETC1S/ETC2AS encodings
Explanation:
ETC1S encoding is a subset of ETC1, which is using only one color endpoint per 4x4 block. The base color is therefore is always encoded as RGB555 and there is no need to encode block flips. ETC2AS encoding is a subset of ETC2A encoding which is using ETC1S encoding for color and default ETC2A encoding for alpha.
ETC1S/ETC2AS Crunch compression and decompression is based on ETC and DXT Crunch compression and decompression algorithms:
- ETC1S/ETC2AS tiling is performed within the area of 8x8 pixels using DXT1/DXT5 tiling scheme
- ETC1S color endpoints are generated using standard ETC1 optimization
- ETC1S color codebook encoding is equivalent to ETC1 codebook encoding
- ETC1S level encoding is equivalent to DXT1 level encoding
- ETC2AS alpha codebook encoding is equivalent to ETC2A alpha codebook encoding
- ETC2AS level encoding is equivalent to DXT5 level encoding
Testing results suggest that ETC1S/ETC2AS encodings can be used to achieve lower bitrates than ETC1/ETC2A on the Kodak test set while providing equivalent image quality (estimated using PSNR).
DXT Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch (revision
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Alexander Suvorov
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3e12aff909 |
Fix miscellaneous compiler warnings
DXT Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch (revision
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Alexander Suvorov
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bec4114bea |
Add compression support for ETC2A textures
This change makes it possible to use Crunch algorithms for ETC textures with Alpha channel.
Explanation:
For simplicity, Crunch algorithms currently do not use ETC2 specific modes (T, H or P). For this reason, the currently used ETC2A compression format is technically equivalent to ETC1 + Alpha. Note that ETC2 encoding is a superset of ETC1, so any texture, which consists of ETC1 color blocks and ETC2 Alpha blocks, can be correctly decoded by an ETC2A (ETC2_RGBA8) decoder.
Compression scheme for ETC2 Alpha blocks is equivalent to the compression scheme for DXT5 Alpha blocks. ETC2 Alpha endpoint clusterization is performed based on the very same output of the Alpha palettizer which is used for DXT5 Alpha. The only part which is actually different is the Alpha endpoint optimization step.
In order to perform ETC2 Alpha encoding, we can first run the already existing algorithm for DXT5 Alpha endpoint optimization, in order to obtain the initial approximate solution. Then the approximate solution is refined based on the ETC2 Alpha modifier table. When performing raw ETC2A encoding, all the 16 ETC2 Alpha modifiers are used during optimization. However, when performing ETC2A quantization, for performance reasons, only 2 Alpha modifiers are currently used (modifier 13, which allows to perform precise approximation on short Alpha intervals, and modifier 11, which has more or less regularly distributed values, and is used for large Alpha intervals).
For compatibility reasons, ETC2 color compression wrappers have also been added to the code, though, as has been mentioned before, at the current moment ETC2 specific modes are not used, so ETC2 color compression is currently equivalent to ETC1 compression.
The ETC decoder functionality has been significantly extended, Crunch is now capable to decode ETC2 and ETC2A textures (input ETC2 textures can have T, H or P blocks).
In order to use ETC2A compression, use the -ETC2A command line option (i.e. "crunch_x64.exe -ETC2A input.png"). By default, compressed ETC2A textures will be decompressed into KTX file format.
DXT Testing:
The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch (revision
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Alexander Suvorov
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f284523b15 |
Add compression support for ETC1 textures
Explanation: Crunch algorithms are normally used for compression of DXTn textures. However, Crunch algorithms are much more powerful, and with some minor adjustments, those algorithms can be directly used to compress other texture formats. For example, the current commit demonstrates how to use the existing Crunch algorithms to compress ETC1 textures. Basics: In general, Crunch is performing the following steps: - tiling (determines block encodings) - quantization of the tile endpoints (determines endpoint indices) - optimization of the endpoints for each tile group (determines endpoint dictionary) - quantization of the selectors (determines selector indices) - selector refinement for each selector group (determines selector dictionary) - compression of the previously determined block encodings, dictionaries and indices Dictionary element: When applying Crunch algorithms to a new texture format, it is necessary to first define the dictionary element. In context of Crunch, this means thats the whole image consists of smaller non-overlapping blocks, while the contents of each individual block is determined by an endpoint and a selector from the corresponding dictionaries. For example, in case of DXT format, each endpoint and selector codebook element corresponds to a 4x4 pixel block. In general, the size of the blocks, which form the encoded image, depends on the texture format and quality considerations. It is proposed to define the dictionaries according to the following limitations: - The dictionary elements should be compatible with the existing Crunch algorithms, while the image blocks defined by those dictionary elements should be compatible with the texture encoding format. - It should be possible to cover a wide range of image quality and bitrates by just changing the size of the endpoint and selector dictionaries. If there is no limitation on the dictionary size, the encoding should preferably become lossless or near-lossless (not considering the quality loss implied by the texture format itself). In case of ETC1, the texture format itself determines the minimal size of the image block, defined by endpoint and selector: it can be either 2x4 or 4x2 rectangle, aligned to the borders of the 4x4 grid. It is not possible to use higher granularity, because each of those rectangles can have only one base color, according to the ETC1 format. For the same reason, any image block, defined by an endpoint and a selector from the dictionary, should be combined from those aligned 2x4 or 4x2 rectangles. Let's investigate the possibilities for the endpoint dictionary. According to the ETC1 format, each 4x4 ETC1 block is split in half, while each ETC1 subblock has it's own base color and a modifier table index. In fact, the base color and the modifier table index simply define the high and the low colors for the subblock (while there are some limitations on the position of those high and low colors, implied by the ETC1 encoding). If we define the endpoint dictionary element in such a way that it contains information about more than one ETC1 base color, then such a dictionary will become incompatible with the existing tile quantization algorithm, and the reason for this is the following. The Crunch tiling algorithm first performs quantization of all the tile pixel colors, down to just 2 colors. Then it quantizes all those color pairs, coming from different tiles. This approach works quite well for 4x4 DXT blocks, as those 2 colors approximately represent the principle component of the tile pixel colors. In case of ETC1 however, mixing together pixels, which correspond to different base colors, does not make much sense, as each group of those pixels has it's own low and high color values, independent from other groups. When those pixels are mixed together, the information about the original principle components of each subblock gets lost. For the mentioned reason, each endpoint dictionary element should correspond to a single ETC1 base color. In such case, the tile quantization algorithm will work almost the same way as for DXT format. Each pair of colors, generated by the tile palletizer, will normally have the subblock base color value somewhere in the middle between those 2 colors, so quantizing those color pairs should also automatically quantize the corresponding base colors. Moreover, each color pair implicitly contains information about the modifier table index (which corresponds to the distance between the high and the low colors), and therefore the corresponding table index will also get automatically quantized. Endpoint and selector dictionary elements, which define a single 2x4 or 4x2 ETC1 subblock, are fully compatible with the existing Crunch algorithms (because each ETC1 subblock is associated with a single base color and a single modifier table index). At the same time, those subblocks are minimal possible blocks, which can be defined by a dictionary element for ETC1 format (as has been shown earlier). Of course, it is also possible to use blocks larger than 2x4 or 4x2 (assuming that all the ETC1 subblocks, which form such a block, will have the same base color and the same modifier table index), however, with a larger block area it would be not possible to achieve near-lossless quality when the dictionary size is not limited. As the result, it is proposed to define the dictionaries in the following way: - Each element of the endpoint dictionary defines a single base color and a single modifier table index of a 2x4 or a 4x2 pixel block (which represents an ETC1 subblock). - Each endpoint is encoded as 3555 (3 bits for the table index and 5 bits for each component of the base color). - Each element of the selector dictionary defines selectors for a 2x4 or a 4x2 block. - Each selector is encoded using 16 bits. ETC1-specific adjustments: In case of DXT, the size of the encoded block is 4x4, while the tiling is performed in a 8x8 area (4 blocks). In case of ETC1, the tiling can be performed either in a 4x4 area (2 blocks), or in a 8x8 area (8 blocks), while other possibilities are either not symmetrical or too complex. For simplicity it is proposed to use 4x4 area for tiling. There are therefore 3 possible encodings: the 4x4 block is not split (encoded with a single endpoint), the 4x4 block is split horizontally, the 4x4 block is split vertically. For simplicity, endpoint references are currently determined only within the tiling area, while the encoding of the endpoint references has been adjusted in the following way: - The first ETC1 subblock will always have the reference value of 0 - The second ETC1 subblock can have the reference value of 0 if it has the same endpoint as the first subblock (note that in such case the flip of the ETC1 block does not need to be defined), the value of 1 if the corresponding ETC1 block is split horizontally, and the value of 2 if the corresponding ETC1 block is split vertically According to the ETC1 format, the base colors within an ETC1 block can be encoded either as 444 and 444, or differentially as 555 and 333. For simplicity, this aspect is currently not taken into account (all the endpoints are encoded as 3555 in the codebook). If it appears that the base colors in the resulting ETC1 block can not be encoded differentially, the decoder will convert both base colors from 555 to 444. At first, it might look like the ETC1 block flipping can bring some complications for Crunch, as the subblock structure might not look like a grid. This can be easily resolved by mirroring all the vertical ETC1 blocks across the main diagonal of the block after the tiling step (so that all the ETC1 subblocks will become 4x2 and form a regular grid). The decoder can mirror the ETC1 selector back according to the decoded block flip. The code adjustments for the ETC1 compression support are pretty straightforward and mostly trivial. Just note that when format-specific adjustments affect performance critical code, it makes sense to duplicate the body of the affected function and perform format-specific optimizations in each copy of the function individually. For performance reasons, the following 4 functions now got both ETC and DTX specific versions: - determine_tiles_task_etc() is an ETC-optimized version of the determine_tiles_task(), where dxt_fast class has been replaced with the etc1_optimizer class. - determine_color_endpoint_codebook_task_etc() is an ETC-optimized version of the determine_color_endpoint_codebook_task(), where dxt1_endpoint_optimizer class has been replaced with the etc1_optimizer class. - pack_color_endpoints_etc() is an ETC-optimized version of the pack_color_endpoints(), where 565565 DXT color endpoint encoding has been replaced with 3555 ETC color endpoint encoding. - unpack_etc1() is an ETC version of the unpack_dxt1() function. The color_quality_power_mul and m_adaptive_tile_color_psnr_derating parameters for ETC1 format have been selected in such a way, so that ETC1 compression gives approximately the same average Luma PSNR as the equivalent DXT1 compression, when compressing the Kodak test set without mipmaps using default quality. In order to use ETC1 compression, use the -ETC1 command line option (i.e. "crunch_x64.exe -ETC1 input.png"). By default, compressed ETC1 textures will be decompressed into KTX file format. DXT Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps using DXT1 encoding] Original: 1582222 bytes / 28.876 sec Modified: 1482780 bytes / 13.255 sec Improvement: 6.28% (compression ratio) / 54.10% (compression time) [Compressing Kodak set with mipmaps using DXT1 encoding] Original: 2065243 bytes / 36.987 sec Modified: 1931586 bytes / 18.068 sec Improvement: 6.47% (compression ratio) / 51.15% (compression time) ETC Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). The ETC1 quantization parameters have been selected in such a way, so that ETC1 compression gives approximately the same average Luma PSNR as the corresponding DXT1 compression (which is equal to 34.044 dB for the Kodak test set compressed without mipmaps using DXT1 encoding and default quality settings). [Compressing Kodak set without mipmaps using ETC1 encoding] Total size: 1887265 bytes Total time: 14.954 sec Average bitrate: 1.600 bpp Average Luma PSNR: 34.049 dB |
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Alexander Suvorov
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eee6b26e5d |
Optimize endpoint and selector sorting algorithms
This change significantly improves compression speed. Explanation: The main ideas used for the endpoint and selector sorting optimization: - unpacked color and alpha endpoints can be cached - pixel selectors can be processed in groups, while the intermediate error results for those groups can be precalculated - instead of maintaining the mask of the processed elements, the remaining elements can be reorganized to form a continuous block on each iteration (the last remaining element is moved into the position of the processed element) - after optimization, endpoint sorting works significantly faster than endpoint reordering, so the overall performance can be improved by moving selector optimization into the endpoint sorting thread Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.863 sec Modified: 1482780 bytes / 14.564 sec Improvement: 6.28% (compression ratio) / 49.54% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.968 sec Modified: 1931586 bytes / 19.717 sec Improvement: 6.47% (compression ratio) / 46.66% (compression time) |
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Alexander Suvorov
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f1d6a5a735 |
Improve and optimize the endpoint reordering algorithm
This change significantly improves the compression ratio and compression speed. Explanation: After the endpoint codebook has been determined, the endpoints can be reordered in order to improve the compression ratio. On the one hand, endpoint indices of the neighbor blocks should be similar, as the encoder compresses the deltas between those neighbour indices. On the other hand, the neighbor endpoints in the codebook should be also similar, as the encoder compresses the deltas between the color components of those neighbor endpoints. The optimization is based on the Zeng's technique, using a weighted function which takes into account both similarity of the endpoint indices for the neighbor blocks and similarity of the neighbor endpoints in the codebook. The similarity of the endpoint indices is optimized using the combined neighborhood frequency of the candidate endpoint and all the currently selected endpoints in the list. The similarity of the neighbor endpoints in the codebook is optimized using euclidian distance from the candidate endpoint to the extremity of selected endpoints list. The original optimization function for the endpoint candidate (i) can be represented as: F(i) = (total_neighborhood_frequency(i) + 1) * (endpoint_similarity(i) + 1) The problem with this approach is the following. While the endpoint_similarity(i) has a limited range of values, the total_neighborhood_frequency(i) grows rapidly with the increasing size of the selected endpoints list. With each iteration this introduces additional disbalance for the weighted function. In order to minimize this effect, is it proposed to normalize the total_neighborhood_frequency(i) on each iteration. For computational simplicity, the normalizer is computed as the optimal total_neighborhood_frequency value from the previous iteration, multiplied by a constant. The modified optimization function can be represented as: F(i) = (total_neighborhood_frequency(i) + total_neighborhood_frequency_normalizer) * (endpoint_similarity(i) + 1) The main ideas used for endpoint reordering optimization: - all the computations, which are common for the endpoint reordering threads, have been moved outside of the threads - the ordering histogram offsets, which point to the neighborhood frequency values for a specific endpoint, are now cached, which reduces the number of multiplications when accessing the histogram - floating point operations have been replaced with integer operations Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.873 sec Modified: 1482726 bytes / 15.791 sec Improvement: 6.29% (compression ratio) / 45.31% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.925 sec Modified: 1931475 bytes / 20.970 sec Improvement: 6.48% (compression ratio) / 43.21% (compression time) |
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Alexander Suvorov
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5822475b22 |
Completely remove all the chunk related code from the encoder and decoder
This change slightly improves compression speed and simplifies further modification of the code. Explanation: Additional performance boost is achieved by using linear representation for selectors and storing block selectors in a single uint32/uint64. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.927 sec Modified: 1494501 bytes / 17.301 sec Improvement: 5.54% (compression ratio) / 40.19% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.992 sec Modified: 1945365 bytes / 22.548 sec Improvement: 5.80% (compression ratio) / 39.05% (compression time) |
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Alexander Suvorov
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e7d458aa22 |
Switch from chunk encoding to block encoding while performing image quantization
This change improves compression speed and simplifies further modification of the code. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.947 sec Modified: 1494501 bytes / 17.642 sec Improvement: 5.54% (compression ratio) / 39.05% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.965 sec Modified: 1945365 bytes / 22.989 sec Improvement: 5.80% (compression ratio) / 37.81% (compression time) |
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Alexander Suvorov
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c9fd4dca75 |
Compute compressed endpoints size without pack simulation
This change improves compression speed. Explanation: While trying different remappings for the endpoint indices, there is no need to perform full pack simulation when using Huffman coding. Once the delta index histogram is generated, it is sufficient to simply multiply the code sizes by the corresponding frequences in order to get the total size of the compressed endpoint indices stream. There is also no need to compute the rest of the compressed stream, as its size does not depend on the endpoint remapping and therefore is always constant, so it will not affect the size comparison during endpoint optimization. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.864 sec Modified: 1494501 bytes / 25.317 sec Improvement: 5.54% (compression ratio) / 12.29% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.927 sec Modified: 1945365 bytes / 33.151 sec Improvement: 5.80% (compression ratio) / 10.23% (compression time) |
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Alexander Suvorov
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d0b6f5759b |
Switch from chunk encoding to block encoding after quantization
This change simplifies further modification of the code. Explanation: Considering that chunks are no longer used in the output format, it makes sense to also remove chunk related code from the intermediate processing. This modification also allows to use endpoint references from the leftmost block to the rightmost block in the previous scanline (wrapped reference to the left). Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.846 sec Modified: 1494501 bytes / 25.628 sec Improvement: 5.54% (compression ratio) / 11.16% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.869 sec Modified: 1945365 bytes / 33.497 sec Improvement: 5.80% (compression ratio) / 9.15% (compression time) |
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Alexander Suvorov
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ef540e54de |
Encode raw selector indices instead of selector indices deltas
This change significantly improves compression ratio and compression speed. Explanation: The original version of Crunch encodes the differences between the neighbour indices in order to get advantage of the neighbour indices similarity. The efficiency of such approach highly depends on the continuity of the encoded data. While neighbour color and alpha endpoints are usualy similar, this is usually not the case for selectors. Of course, in some situations, encoding deltas for selector indices makes sense, for example, when the image contains a lot of regular patterns (except the special case of completely flat areas, where using selector deltas does not bring much advantage). In any case, such situations are relatively rare, so it usually appears to be more efficient to encode raw selector indices. Note that when not using deltas for selector indices, the remapping of the selector indices no longer affects the size of the encoded selector indices stream (at least when using Huffman coding). This makes the Zeng optimization step unnecessary, and it is sufficient to simply optimize the size of the packed selector codebook. Note: This modification alters the output file format and makes it incompatible with the previous revisions. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.845 sec Modified: 1521167 bytes / 26.048 sec Improvement: 3.86% (compression ratio) / 9.70% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.949 sec Modified: 1977373 bytes / 33.889 sec Improvement: 4.25% (compression ratio) / 8.28% (compression time) |
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Alexander Suvorov
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974fab40a5 |
Switch from the chunk encoding concept to the reference encoding concept
This change improves the compression ratio. Explanation: In the original version of Crunch all the blocks are grouped into chunks of 2x2 blocks. Each chunk can have one of 8 different types. The type of the chunk determines which blocks inside the chunk share the same endpoints (for example, all the blocks inside the chunk share the same endpoints, or blocks in the right column share the same endpoints, or all the blocks have different endpoints, etc.). Encoding of endpoints equality is usually cheaper than encoding of duplicate endpoint indices. The used 8 chunk types do not cover all the possibilities, but they can be efficiently encoded using 0.75 bits per block (uncompressed). The modified algorithm no longer uses the concept of chunks in the output file format and is based on an alternative approach. Endpoints for each block can be either copied from the left nearest block (reference to the left), copied from the upper nearest block (reference to the top), or decoded from the stream (reference to itself). Note that this is a superset of the original encoding, so all the images previously encoded with the original algorithm can be losslessly transcoded into the new format, but not vice versa. Even though the new endpoint equality encoding is more expensive (about 1.58 bits per block, uncompressed), it provides more flexibility for endpoint matching inside the former "chunks", and more importantly, it allows to inherit endpoints from outside the former "chunks" (which is not possible when using the original chunk encoding). The blocks are no longer grouped together and are encoded in the same order as they appear on the image. Note: This modification alters the output file format and makes it incompatible with the previous revisions. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.903 sec Modified: 1548791 bytes / 28.818 sec Improvement: 2.11% (compression ratio) / 0.29% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.978 sec Modified: 2017245 bytes / 36.846 sec Improvement: 2.32% (compression ratio) / 0.36% (compression time) |
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Alexander Suvorov
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178742ca6f |
Remove linear lists of endpoint and selector indices
Explanation: After switching to ordering histograms, the linear lists of endpoint and selector indices are no longer used in Zeng function, and therefore can be removed. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.872 sec Modified: 1561622 bytes / 28.434 sec Improvement: 1.30% (compression ratio) / 1.52% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.910 sec Modified: 2033151 bytes / 36.369 sec Improvement: 1.55% (compression ratio) / 1.47% (compression time) |
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Alexander Suvorov
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8cc5f19ae5 |
Use left nearest block for endpoint index prediction
This change improves compression ratio. Explanation: In the original algorithm the relative position of the block, used for prediction of the endpoint index for the currently decoded block, depends on the chunk encoding type. It can be a horizontal neighbour, a vertical neighbour, a diagonal neighbour, or in some rare cases even a block at relative position (-2, 0) or (-3, 0). Using left nearest neighbour for endpoint index prediction for each block (except the blocks at the image borders) minimizes the average distance to the prediction block and therefore usually improves the endpoint index prediction. Note: This modification alters the output file format and makes it incompatible with the previous revisions. Testing: The modified algorithm has been tested on the Kodak test set using 64-bit build with default settings (running on Windows 10, i7-4790, 3.6GHz). All the decompressed test images are identical to the images being compressed and decompressed using original version of Crunch. [Compressing Kodak set without mipmaps] Original: 1582222 bytes / 28.838 sec Modified: 1570534 bytes / 28.629 sec Improvement: 0.74% (compression ratio) / 0.72% (compression time) [Compressing Kodak set with mipmaps] Original: 2065243 bytes / 36.977 sec Modified: 2051509 bytes / 36.568 sec Improvement: 0.67% (compression ratio) / 1.11% (compression time) |
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Alexander Suvorov
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d34192aa07 | Split the header block from the crn_decomp.h into a separate crn_defs.h file. This change makes the used CRND_HEADER_FILE_ONLY macro unneccesary. | ||
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Alexander Suvorov
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7c02055d05 | Reformat the source files. The source files have been reformatted using: clang-format.exe -style="{BasedOnStyle: Google, AllowAllParametersOfDeclarationOnNextLine: false, AllowShortFunctionsOnASingleLine: Inline, AllowShortIfStatementsOnASingleLine: false, AllowShortLoopsOnASingleLine: false, ColumnLimit: 0, DerivePointerAlignment: false, SortIncludes: false}" | ||
 richgel99@gmail.com
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f63e26aee6 | v1.03 prerelease - Full Linux port of crnlib/crunch, in progress - still more testing to do, and some cmd line options (such as -timestamp) don't work under linux yet, but the core stuff (compression/decompression/transcoding) should work fine and performance under Linux is comparable to Windows. The 3 examples haven't been ported yet. | ||
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richgel99@gmail.com
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9f98ea7e22 |