Ytdyklly

The aim of my code is to decode an image format that is based on the JPEG chain of compression/decompression, however it is not compatible with the default JPEG flow as far as I know, since all libraries I have tried fail to properly decode the data. I am only interested decompression in this case. It follows the standard pattern:

• Read Huffman values -> Like normal JPEG


• unzigzag -> Like normal JPEG


• Dequantize -> Like normal JPEG


• IDCT -> Almost like normal JPEG, but different range/clamping


• Color Space conversion -> Custom, not YCbCr

For one 8x8 except for the last step that looks like this right now:

int16_t processBlock(int16_t prevDc, BitStream &stream, const tHuffTable &dcTable, const tHuffTable &acTable,

                     float *quantTable, bool isLuminance, int16_t *outBlock) {

    int16_t workBlock[64] = {0};

    int16_t curDc = decodeBlock(stream, workBlock, dcTable, acTable, prevDc); 

    unzigzag(workBlock);

    dequantize(workBlock, quantTable);

    idct(outBlock, workBlock, isLuminance);

    return curDc;

}

after this the outBlock is treated by the color space conversion based on the image type.

What I want to optimize is the overall performance. The entire image is decompressed in the following way with 4 luminance blocks for component 1, 1 chrominance block for component 2 and 1 chrominance block for component 3. There are 4 more blocks for another luminance component, but I dont know what it is used for, so we can ignore it. The code looks like this:

void decodeImageType0(

        uint32_t width,

        uint32_t height,

        std::vector<uint8_t> &outData,

        BitStream &stream,

        const tHuffTable &dcLumTable,

        const tHuffTable &acLumTable,

        const tHuffTable &dcCromTable,

        const tHuffTable &acCromTable,

        float *lumQuant[4],

        float *cromQuant[4]) {

    int16_t lum0[4][64]{};

    int16_t lum1[4][64]{};

    int16_t crom0[64]{};

    int16_t crom1[64]{};

    uint32_t colorBlock[16 * 16]{};



    const auto actualHeight = ((height + 15) / 16) * 16;

    const auto actualWidth = ((width + 15) / 16) * 16;



    int16_t prevDc[4] = {0};

    for (auto y = 0; y < (actualHeight / 16); ++y) {

        for (auto x = 0; x < (actualWidth / 16); ++x) {

            for (auto &lum : lum0) {

                prevDc[0] = processBlock(prevDc[0], stream, dcLumTable, acLumTable, lumQuant[0], true, lum);

            }

            prevDc[1] = processBlock(prevDc[1], stream, dcCromTable, acCromTable, cromQuant[1], false, crom0);

            prevDc[2] = processBlock(prevDc[2], stream, dcCromTable, acCromTable, cromQuant[2], false, crom1);

            for (auto &lum : lum1) {

                prevDc[3] = processBlock(prevDc[3], stream, dcLumTable, acLumTable, lumQuant[3], true, lum);

            }



            decodeColorBlockType0(lum0, lum1, crom0, crom1, colorBlock);

            for (auto row = 0; row < 16; ++row) {

                if(y * 16 + row >= height || x * 16 >= width) {

                    continue;

                }



                const auto numPixels = std::min(16u, width - x * 16);

                memcpy(outData.data() + (y * 16 + row) * width * 4 + x * 16 * 4, &colorBlock[row * 16], numPixels * 4);

            }

        }

    }

}

Now my measurements have shown that over 80% of the time is spent inside the idct function, so this is where I want to optimize. The function looks like this, after I applied what I could think of to optimize it. I have created a cache of the static coefficients used in the IDCT process which significantly improved performance, but I hope there is still room for more, for example nanojpg is 3 times faster (however with invalid results).

float idctHelper(const int16_t *inBlock, int32_t u, int32_t v, int32_t blockWidth, int32_t blockHeight) {

    glm::vec<4, float, glm::packed_lowp> vec3{};



    float result = 0.0f;

    for (auto y = 0; y < blockHeight; ++y) {

        for (auto x = 0; x < blockWidth; x += 4) {

            const auto idx = (v * 8 + u) * 64 + y * 8 + x;

            vec3 = glm::vec<4, float, glm::packed_lowp>(inBlock[y * blockWidth + x], inBlock[y * blockWidth + x + 1], inBlock[y * blockWidth + x + 2], inBlock[y * blockWidth + x + 3]) *

                    glm::vec<4, float, glm::packed_lowp>(idctLookup[idx], idctLookup[idx + 1], idctLookup[idx + 2], idctLookup[idx + 3]);

            result += vec3.x + vec3.y + vec3.z + vec3.w;

        }

    }



    return result;

}



template<typename T, typename U = T>

U clamp(T value, T min, T max) {

    return static_cast<U>(std::min<T>(std::max<T>(value, min), max));

}



void idct(int16_t *outBlock, int16_t *inBlock, bool isLuminance, int32_t blockWidth = 8, int32_t blockHeight = 8) {

    for (auto y = 0; y < blockHeight; ++y) {

        for (auto x = 0; x < blockWidth; ++x) {

            auto value = static_cast<int16_t>(std::round(

                    0.25f * idctHelper(inBlock, x, y, blockWidth, blockHeight)));

            if (isLuminance) {

                value = clamp<int16_t>(static_cast<int16_t>(value + 128), 0, 255);

            } else {

                value = clamp<int16_t>(value, -256, 255);

            }



            outBlock[y * blockWidth + x] = value;

        }

    }

}

This is the cache that is created once:

float alphaFunction(int32_t n) {

    static float INV_SQRT_2 = 1.0f / sqrtf(2.0f);



    if (n == 0) {

        return INV_SQRT_2;

    } else {

        return 1;

    }

}

        for (auto u = 0; u < 8; ++u) {

            for (auto v = 0; v < 8; ++v) {

                for (auto x = 0; x < 8; ++x) {

                    for (auto y = 0; y < 8; ++y) {

                        idctLookup[(v * 8 + u) * 64 + y * 8 + x] = alphaFunction(x) * alphaFunction(y) *

                                                                   cosf((2 * u + 1) * x * (float) M_PI / 16.0f) *

                                                                   cosf((2 * v + 1) * y * (float) M_PI / 16.0f);

                    }

                }

            }

        }